Intelligent light storage charging and detecting energy coordination optimization control method and system

Abstract

This invention relates to an intelligent photovoltaic-storage-charging-monitoring energy collaborative optimization control method and system. The method includes: predicting the high-frequency fluctuation cycle of each charging station based on historical information; when the photovoltaic power generation in the charging station is in a high-frequency fluctuation cycle at the current moment, determining the monitoring frequency of meteorological data according to the average duration of each fluctuation predicted by the fluctuation prediction model, and adjusting the power supply mode according to the meteorological data; when the photovoltaic power generation in the charging station is not in a high-frequency fluctuation cycle at the current moment, determining that the monitoring frequency of meteorological data is a fixed preset frequency. This invention dynamically adjusts the monitoring frequency and power supply mode to cope with the volatility of photovoltaic power generation and the anomalies of meteorological data. This strategy optimization not only improves the stability and reliability of the system, but also reduces operating costs and the impact on the power grid; it can effectively improve the operating efficiency and stability of charging stations and reduce the impact of photovoltaic power generation fluctuations and meteorological anomalies on electric vehicle charging.

Description

Technical Field

[0001] This invention relates to the field of energy control technology, specifically to an intelligent photovoltaic, energy storage, charging, and inspection energy collaborative optimization control method and system. Background Technology

[0002] Photovoltaic-storage-charging-inspection (PV-SCI) is a new generation of charging infrastructure integrating photovoltaic power generation, energy storage systems, charging functions, and battery testing. Photovoltaic power generation utilizes photovoltaic panels to generate solar energy above parking spaces or within a designated area. The energy storage system stores electrical energy through an intelligent energy storage battery system (such as lithium iron phosphate batteries) and releases it when needed. The charging function provides charging services to electric vehicles and other electrical end-users. Battery testing simultaneously monitors the electric vehicle's battery during charging, providing battery health reports and risk warnings. However, solar power generation is intermittent and unpredictable, especially in summer when weather is unpredictable and may suddenly become unusable. This is because solar power generation depends on sunlight, and the intensity and duration of sunlight are unstable, causing fluctuations in power generation. Therefore, this invention proposes an intelligent PV-storage-charging-inspection energy collaborative optimization control method and system. Summary of the Invention

[0003] This invention provides an intelligent photovoltaic energy storage charging and inspection collaborative optimization control method and system, which solves the technical problems mentioned in the background art.

[0004] To achieve the above objectives, the present invention provides the following technical solution:

[0005] A method for intelligent photovoltaic, energy storage, charging, and inspection coordinated optimization control, the method comprising the following steps:

[0006] Collect historical information on photovoltaic power generation in charging stations, and predict the high-frequency fluctuation cycle of each charging station based on the historical information;

[0007] When the photovoltaic power generation in the charging station is in a high-frequency fluctuation cycle at the current moment, the monitoring frequency of meteorological data is determined according to the average duration of each fluctuation predicted by the fluctuation prediction model, and the power supply mode is adjusted according to the meteorological data. The power supply mode includes a first power supply mode and a second power supply mode.

[0008] When the photovoltaic power generation in the charging station is not in a high-frequency fluctuation cycle at the current moment, the monitoring frequency of the meteorological data is determined to be a fixed preset frequency, and the power supply mode is adjusted according to the meteorological data.

[0009] As a further technical solution of the present invention, the step of collecting historical information on photovoltaic power generation in charging stations and predicting the high-frequency fluctuation period of each charging station based on the historical information includes:

[0010] Historical information on photovoltaic power generation in charging stations is collected, and the historical information on photovoltaic power generation in charging stations is used to train a neural network model to obtain a fluctuation prediction model;

[0011] The fluctuation information of photovoltaic power generation in each charging station within each cycle is predicted based on the fluctuation prediction model. The fluctuation information includes the number of fluctuations of photovoltaic power generation in each charging station and the average duration of each fluctuation.

[0012] Based on the number of fluctuations in the fluctuation information and the preset fluctuation threshold, it is determined whether the photovoltaic power generation of each power station is a high-frequency fluctuation cycle in each period.

[0013] Among them, "fluctuation" refers to a situation where the output power of photovoltaic power generation does not meet the preset output power and the duration of the fluctuation exceeds the preset duration, which can be counted as a fluctuation.

[0014] As a further technical solution of the present invention, the step of determining the monitoring frequency of meteorological data based on the average duration of each fluctuation predicted by the fluctuation prediction model when the photovoltaic power generation in the charging station is in a high-frequency fluctuation cycle at the current moment, and adjusting the power supply mode according to the meteorological data, wherein the power supply mode includes a first power supply mode and a second power supply mode, includes:

[0015] When the photovoltaic power generation in the charging station is in a high-frequency fluctuation cycle at the current moment, the monitoring frequency of meteorological data is determined according to the average duration of each fluctuation predicted by the fluctuation prediction model.

[0016] When an anomaly is detected in the meteorological data, the power supply mode is switched to the second power supply mode, which continues until the anomaly ends. The power supply mode will be restored from the second power supply mode to the first power supply mode within minutes. The value of is determined based on the number of fluctuations within the current high-frequency fluctuation cycle predicted by the fluctuation prediction model. The number of fluctuations is related to... The values ​​of are positively correlated; the greater the number of fluctuations, the higher the corresponding . The larger the value of ;

[0017] The first power supply mode is: powering electric vehicles mainly through photovoltaic power generation and supplemented by energy storage system. If there is a surplus of electricity generated by photovoltaic power generation after meeting the power supply needs of electric vehicles, the surplus electricity generated by photovoltaic power generation will be stored in the energy storage system.

[0018] The second power supply mode is: power is supplied to electric vehicles by a combination of energy storage system as the main source and grid as the auxiliary source. In this mode, photovoltaic power generation does not supply power to electric vehicles, but stores electrical energy through the energy storage system.

[0019] As a further technical solution of the present invention, the step of determining that the monitoring frequency of meteorological data is a fixed preset frequency when the photovoltaic power generation in the charging station is not in a high-frequency fluctuation cycle at the current moment, and adjusting the power supply mode according to the meteorological data, includes:

[0020] When the photovoltaic power generation in the charging station is not in a high-frequency fluctuation cycle, the monitoring frequency of meteorological data is determined to be a fixed preset frequency.

[0021] When an anomaly is detected in the meteorological data, the power supply mode is switched to the second power supply mode, which continues until the anomaly ends. The power supply mode will be restored from the second power supply mode to the first power supply mode within minutes; The value of is determined based on the number of fluctuations within the current period predicted by the fluctuation prediction model. The number of fluctuations here is related to... The values ​​of are positively correlated.

[0022] Another objective of this invention is to provide an intelligent photovoltaic-storage-charging-detection energy collaborative optimization control system, the system comprising:

[0023] The high-frequency prediction module is used to collect historical information on photovoltaic power generation in the charging station and predict the high-frequency fluctuation period of each charging station based on the historical information.

[0024] The high-frequency adjustment module is used to determine the monitoring frequency of meteorological data based on the average duration of each fluctuation predicted by the fluctuation prediction model when the photovoltaic power generation in the charging station is in a high-frequency fluctuation cycle at the current moment, and to adjust the power supply mode according to the meteorological data. The power supply mode includes a first power supply mode and a second power supply mode.

[0025] The low-frequency adjustment module is used to determine that the monitoring frequency of meteorological data is a fixed preset frequency when the photovoltaic power generation in the charging station is not in a high-frequency fluctuation cycle at the current moment, and adjust the power supply mode according to the meteorological data.

[0026] As a further technical solution of the present invention, the high-frequency prediction module includes:

[0027] The prediction model building unit is used to collect historical information on photovoltaic power generation in the charging station, and to train the neural network model using the historical information on photovoltaic power generation in the charging station to obtain the fluctuation prediction model.

[0028] The fluctuation information prediction unit is used to predict the fluctuation information of photovoltaic power generation in each charging station within each cycle according to the fluctuation prediction model. The fluctuation information includes the number of fluctuations of photovoltaic power generation in each charging station and the average duration of each fluctuation.

[0029] The high-frequency cycle determination unit is used to determine whether the photovoltaic power generation of each power station is a high-frequency fluctuation cycle in each cycle based on the number of fluctuations in the fluctuation information and the preset fluctuation threshold.

[0030] Among them, "fluctuation" refers to a situation where the output power of photovoltaic power generation does not meet the preset output power and the duration of the fluctuation exceeds the preset duration, which can be counted as a fluctuation.

[0031] As a further technical solution of the present invention, the high-frequency adjustment module includes:

[0032] The first monitoring frequency determination unit is used to determine the monitoring frequency of meteorological data based on the average duration of each fluctuation predicted by the fluctuation prediction model when the photovoltaic power generation in the charging station is in a high-frequency fluctuation cycle at the current moment.

[0033] The first power supply mode switching unit is used to switch the power supply mode to the second power supply mode when an anomaly is detected in the meteorological data, until the anomaly ends. The power supply mode will be restored from the second power supply mode to the first power supply mode within minutes. The value of is determined based on the number of fluctuations within the current high-frequency fluctuation cycle predicted by the fluctuation prediction model. The number of fluctuations is related to... The values ​​of are positively correlated; the greater the number of fluctuations, the higher the corresponding . The larger the value of ;

[0034] The first power supply mode is: powering electric vehicles mainly through photovoltaic power generation and supplemented by energy storage system. If there is a surplus of electricity generated by photovoltaic power generation after meeting the power supply needs of electric vehicles, the surplus electricity generated by photovoltaic power generation will be stored in the energy storage system.

[0035] The second power supply mode is: power is supplied to electric vehicles by a combination of energy storage system as the main source and grid as the auxiliary source. In this mode, photovoltaic power generation does not supply power to electric vehicles, but stores electrical energy through the energy storage system.

[0036] As a further technical solution of the present invention, the low-frequency adjustment module includes:

[0037] The second monitoring frequency determination unit is used to determine that the monitoring frequency of meteorological data is a fixed preset frequency when the photovoltaic power generation in the charging station is not in a high-frequency fluctuation cycle.

[0038] The second power supply mode switching unit is used to switch to the second power supply mode when an anomaly is detected in the meteorological data, until the anomaly ends. The power supply mode will be restored from the second power supply mode to the first power supply mode within minutes; The value of is determined based on the number of fluctuations within the current period predicted by the fluctuation prediction model. The number of fluctuations here is related to... The values ​​of are positively correlated.

[0039] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention provides an intelligent photovoltaic-storage-charging-inspection energy collaborative optimization control method and system. In this invention, a fluctuation prediction model is used to predict the fluctuation information of photovoltaic power generation in each charging station within each cycle. Based on the fluctuation information, it is determined whether each cycle is a high-frequency fluctuation cycle. When the photovoltaic power generation in the charging station is in a high-frequency fluctuation cycle at the current moment, the monitoring frequency for meteorological data is determined by the average monitoring duration of each fluctuation. When the photovoltaic power generation in the charging station is not in a high-frequency fluctuation cycle at the current moment, the monitoring frequency for meteorological data is determined to be a fixed preset frequency, and the power supply mode is adjusted according to the meteorological data. This invention dynamically adjusts the monitoring frequency and power supply mode to cope with the fluctuation of photovoltaic power generation and the anomalies of meteorological data. This strategy optimization not only improves the stability and reliability of the system but also reduces operating costs and the impact on the power grid; it can effectively improve the operating efficiency and stability of charging stations and reduce the impact of photovoltaic power generation fluctuations and meteorological anomalies on electric vehicle charging. Attached Figure Description

[0040] Figure 1 This is a flowchart of a smart photovoltaic-storage-charging-detection energy collaborative optimization control method.

[0041] Figure 2 This is a flowchart of the step for predicting high-frequency fluctuation cycles in an intelligent photovoltaic-storage-charging-detection energy collaborative optimization control method.

[0042] Figure 3 This is a flowchart illustrating the power supply adjustment steps within a high-frequency fluctuation cycle in an intelligent photovoltaic-storage-charging-detection energy collaborative optimization control method.

[0043] Figure 4 This is a flowchart illustrating the power supply adjustment steps during non-high-frequency fluctuation cycles in a smart photovoltaic-storage-charging-detection energy collaborative optimization control method.

[0044] Figure 5 This is a structural block diagram of an intelligent photovoltaic-storage-charging-detection energy collaborative optimization control system. Detailed Implementation

[0045] To make the technical problems to be solved, the technical solutions, and the beneficial effects of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present invention and are not intended to limit the present invention.

[0046] like Figure 1As shown in the figure, this invention provides a method for intelligent photovoltaic energy storage, charging, and inspection collaborative optimization control, the method comprising the following steps:

[0047] Step S100: Collect historical information on photovoltaic power generation in the charging station, and predict the high-frequency fluctuation period of each charging station based on the historical information; please refer to [link / reference] for details. Figure 2 .

[0048] Step S101: Collect historical information on photovoltaic power generation in the charging station, and use the historical information on photovoltaic power generation in the charging station to train the neural network model to obtain a fluctuation prediction model;

[0049] The specific steps for establishing a fluctuation prediction model include: collecting historical photovoltaic power generation data from the monitoring systems of each charging station. This data should include the output power of photovoltaic power generation and the corresponding timestamp. The data collection period can be set according to actual needs, such as weekly or monthly. The collected data is cleaned to remove outliers, missing values, or duplicates, ensuring the accuracy and completeness of the data. The data is preprocessed based on preset output power and fluctuation duration thresholds. Data segments where the photovoltaic power output power does not meet the preset output power and the fluctuation duration exceeds the preset duration are identified and marked as fluctuations. The number of fluctuations and the average duration of each fluctuation for each charging station within each historical period are statistically analyzed. The preprocessed data is divided into training and validation sets. Features are extracted from the preprocessed data, including historical fluctuation frequency, average duration, time (e.g., hour, date, season), weather conditions (e.g., sunny, cloudy, rainy), temperature, and humidity. Feature selection should be based on the actual situation and the needs of the neural network model. Suitable models for processing time series data include Recurrent Neural Networks (RNNs), Long Short-Term Memory Networks (LSTMs), and Gated Recurrent Units (GRUs). Use deep learning frameworks (such as TensorFlow, PyTorch, etc.) to build neural network models. Train the neural network model using training set data, and evaluate the model's performance using validation set data until the preset performance requirements are met, thus obtaining a trained fluctuation prediction model.

[0050] Step S102: Based on the fluctuation prediction model, the fluctuation information of photovoltaic power generation in each charging station within each cycle is predicted. The fluctuation information includes the number of fluctuations of photovoltaic power generation in each charging station and the average duration of each fluctuation. In this step, by inputting the time corresponding to each cycle within a year into the fluctuation prediction model, the fluctuation information of photovoltaic power generation in each charging station within each cycle within a year can be output.

[0051] Step S103: Based on the number of fluctuations in the fluctuation information and the preset fluctuation threshold, determine whether the photovoltaic power generation of each power station is a high-frequency fluctuation cycle in each period.

[0052] Among them, "fluctuation" refers to: the output power of photovoltaic power generation does not meet the preset output power, and the duration of the fluctuation exceeds the preset duration, which can be counted as a fluctuation; the time step of a cycle may be a week, a month, or a season. A high-frequency fluctuation cycle refers to a cycle in which the number of fluctuations exceeds the preset fluctuation threshold.

[0053] Step S200: When the photovoltaic power generation in the charging station is in a high-frequency fluctuation cycle at the current moment, the monitoring frequency of meteorological data is determined according to the average duration of each fluctuation predicted by the fluctuation prediction model, and the power supply mode is adjusted according to the meteorological data. The power supply mode includes a first power supply mode and a second power supply mode; please refer to [link / reference] for details. Figure 3 .

[0054] Step S201: When the photovoltaic power generation in the charging station is in a high-frequency fluctuation cycle at the current moment, the monitoring frequency of meteorological data is determined according to the average duration of each fluctuation predicted by the fluctuation prediction model. Here, the average duration refers to the average duration of each fluctuation within the current high-frequency fluctuation cycle predicted by the fluctuation prediction model. The larger the average duration, the lower the corresponding monitoring frequency; the smaller the average duration, the higher the corresponding monitoring frequency. The average duration and the monitoring frequency are negatively correlated.

[0055] Step S202: When an anomaly is detected in the meteorological data, the power supply mode is adjusted to the second power supply mode until the anomaly ends. The power supply mode will be restored from the second power supply mode to the first power supply mode within minutes. The value of is determined based on the number of fluctuations within the current high-frequency fluctuation cycle predicted by the fluctuation prediction model. The number of fluctuations here is related to... The values ​​of are positively correlated; the greater the number of fluctuations, the higher the corresponding . The larger the value, the more likely it is to be abnormal. Determining whether meteorological data is abnormal involves the following steps: Continuously collect meteorological data such as sunlight intensity using sensors or weather stations, and preprocess it: Clean the collected data, remove noise and outliers (such as extreme values ​​caused by sensor malfunctions), and perform necessary smoothing; Set abnormal conditions for sunlight intensity based on historical data and operational needs. For example, if sunlight intensity suddenly drops below a certain threshold and remains below it for a preset duration (such as several consecutive minutes), it is considered abnormal; Monitor the preprocessed meteorological data in real time to ensure data accuracy and timeliness. Compare the real-time monitored sunlight intensity with the set threshold; if the abnormal conditions are met, determine that the meteorological data is abnormal and adjust the power supply mode.

[0056] The first power supply mode is as follows: powering electric vehicles mainly through photovoltaic power generation and supplemented by energy storage system (specifically, prioritizing the use of electricity generated by photovoltaic power generation, and when the electricity generated by photovoltaic power generation is insufficient to support the power supply of electric vehicles, the energy storage system assists in powering electric vehicles to meet charging needs). At this time, if there is a surplus of electricity generated by photovoltaic power generation after meeting the power supply needs of electric vehicles, the surplus electricity generated by photovoltaic power generation will be stored in the energy storage system.

[0057] The second power supply mode is: powering electric vehicles by using an energy storage system as the main source and the power grid as a supplement (specifically: prioritizing the use of the energy storage system to power electric vehicles, and using the power grid to power electric vehicles when the remaining power in the energy storage system is less than the preset power). In this mode, photovoltaic power generation does not supply power to electric vehicles, and the photovoltaic power generation stores electrical energy through the energy storage system.

[0058] Step S300: When the photovoltaic power generation in the charging station is not in a high-frequency fluctuation cycle at the current moment, it is determined that the monitoring frequency of the meteorological data is a fixed preset frequency, and the power supply mode is adjusted according to the meteorological data. Please refer to [link / reference] for details. Figure 4 .

[0059] Step S301: When the photovoltaic power generation in the charging station is not in a high-frequency fluctuation cycle, the monitoring frequency of the meteorological data is determined to be a fixed preset frequency; the fixed preset frequency is usually less than the monitoring frequency determined based on the average duration.

[0060] Step S302: When an anomaly is detected in the meteorological data, the power supply mode is switched to the second power supply mode until the anomaly ends. The power supply mode will be restored from the second power supply mode to the first power supply mode within minutes; The value of is determined based on the number of fluctuations predicted by the fluctuation prediction model within the current period. The number of fluctuations is related to... The values ​​of are positively correlated; the greater the number of fluctuations, the higher the corresponding . The larger the value, the more likely it is to be. Less than .

[0061] In this invention, a fluctuation prediction model is used to predict the fluctuation information of photovoltaic power generation in each charging station within each cycle. Based on the fluctuation information, it is determined whether each cycle is a high-frequency fluctuation cycle. When the photovoltaic power generation in the charging station is in a high-frequency fluctuation cycle at the current moment, the monitoring frequency of meteorological data is determined by the average monitoring duration of each fluctuation. When the photovoltaic power generation in the charging station is not in a high-frequency fluctuation cycle at the current moment, the monitoring frequency of meteorological data is determined to be a fixed preset frequency, and the power supply mode is adjusted according to the meteorological data. In this invention, the monitoring frequency and power supply mode are dynamically adjusted to cope with the fluctuation of photovoltaic power generation and the anomalies of meteorological data. This strategy optimization not only improves the stability and reliability of the system, but also reduces operating costs and the impact on the power grid; it can effectively improve the operating efficiency and stability of charging stations and reduce the impact of photovoltaic power generation fluctuations and meteorological anomalies on electric vehicle charging.

[0062] Please see Figure 5 Another objective of this invention is to provide an intelligent photovoltaic-storage-charging-detection energy collaborative optimization control system, the system comprising:

[0063] The high-frequency prediction module 100 is used to collect historical information on photovoltaic power generation in the charging station and predict the high-frequency fluctuation period of each charging station based on the historical information.

[0064] The high-frequency adjustment module 200 is used to determine the monitoring frequency of meteorological data based on the average duration of each fluctuation predicted by the fluctuation prediction model when the photovoltaic power generation in the charging station is in a high-frequency fluctuation cycle at the current moment, and to adjust the power supply mode according to the meteorological data. The power supply mode includes a first power supply mode and a second power supply mode.

[0065] The low-frequency adjustment module 300 is used to determine that the monitoring frequency of meteorological data is a fixed preset frequency when the photovoltaic power generation in the charging station is not in a high-frequency fluctuation cycle at the current moment, and adjust the power supply mode according to the meteorological data.

[0066] In a preferred embodiment of the present invention, the high-frequency prediction module 100 includes:

[0067] The prediction model building unit is used to collect historical information on photovoltaic power generation in the charging station, and to train the neural network model using the historical information on photovoltaic power generation in the charging station to obtain the fluctuation prediction model.

[0068] The fluctuation information prediction unit is used to predict the fluctuation information of photovoltaic power generation in each charging station within each cycle according to the fluctuation prediction model. The fluctuation information includes the number of fluctuations of photovoltaic power generation in each charging station and the average duration of each fluctuation.

[0069] The high-frequency cycle determination unit is used to determine whether the photovoltaic power generation of each power station is a high-frequency fluctuation cycle in each cycle based on the number of fluctuations in the fluctuation information and the preset fluctuation threshold.

[0070] Among them, "fluctuation" refers to: the output power of photovoltaic power generation does not meet the preset output power, and the duration of the fluctuation exceeds the preset duration, which can be counted as a fluctuation; the high-frequency fluctuation cycle may be one month or one season.

[0071] In a preferred embodiment of the present invention, the high-frequency adjustment module 200 includes:

[0072] The first monitoring frequency determination unit is used to determine the monitoring frequency of meteorological data based on the average duration of each fluctuation predicted by the fluctuation prediction model when the photovoltaic power generation in the charging station is in a high-frequency fluctuation cycle at the current moment. The larger the average duration, the lower the corresponding monitoring frequency; the smaller the average duration, the higher the corresponding monitoring frequency. The average duration and the monitoring frequency are negatively correlated.

[0073] The first power supply mode switching unit is used to switch the power supply mode to the second power supply mode when an anomaly is detected in the meteorological data, until the anomaly ends. The power supply mode will be restored from the second power supply mode to the first power supply mode within minutes. The value of is determined based on the number of fluctuations within the current high-frequency fluctuation cycle predicted by the fluctuation prediction model. The number of fluctuations is related to... The values ​​of are positively correlated; the greater the number of fluctuations, the higher the corresponding . The larger the value of ;

[0074] The first power supply mode is as follows: powering electric vehicles mainly through photovoltaic power generation and supplemented by energy storage system (specifically, prioritizing the use of electricity generated by photovoltaic power generation, and when the electricity generated by photovoltaic power generation is insufficient to support the power supply of electric vehicles, the energy storage system assists in powering electric vehicles to meet charging needs). At this time, if there is a surplus of electricity generated by photovoltaic power generation after meeting the power supply needs of electric vehicles, the surplus electricity generated by photovoltaic power generation will be stored in the energy storage system.

[0075] The second power supply mode is: powering electric vehicles by using an energy storage system as the main source and the power grid as a supplement (specifically: prioritizing the use of the energy storage system to power electric vehicles, and using the power grid to power electric vehicles when the remaining power in the energy storage system is less than the preset power). In this mode, photovoltaic power generation does not supply power to electric vehicles, and the photovoltaic power generation stores electrical energy through the energy storage system.

[0076] In a preferred embodiment of the present invention, the low-frequency adjustment module 300 includes:

[0077] The second monitoring frequency determination unit is used to determine the monitoring frequency of meteorological data as a fixed preset frequency when the photovoltaic power generation in the charging station is not in a high-frequency fluctuation cycle; the fixed preset frequency is usually lower than the monitoring frequency determined based on the average duration.

[0078] The second power supply mode switching unit is used to switch to the second power supply mode when an anomaly is detected in the meteorological data, until the anomaly ends. The power supply mode will be restored from the second power supply mode to the first power supply mode within minutes; The value of is determined based on the number of fluctuations predicted by the fluctuation prediction model within the current period. The number of fluctuations is related to... The values ​​of are positively correlated; the greater the number of fluctuations, the higher the corresponding . The larger the value, the better.

[0079] It should be noted that, in this document, the term "comprising" or any other variation thereof is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0080] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A method for intelligent photovoltaic-storage-charging-detection energy collaborative optimization control, characterized in that, The method includes the following steps: Collect historical information on photovoltaic power generation in charging stations, and predict the high-frequency fluctuation cycle of each charging station based on the historical information; When the photovoltaic power generation in the charging station is in a high-frequency fluctuation cycle at the current moment, the monitoring frequency of meteorological data is determined according to the average duration of each fluctuation predicted by the fluctuation prediction model. The power supply mode is adjusted according to the meteorological data. The power supply mode includes a first power supply mode and a second power supply mode. Here, "fluctuation" means that the output power of photovoltaic power generation does not meet the preset output power and the fluctuation duration exceeds the preset duration, which can be counted as a fluctuation. When the photovoltaic power generation in the charging station is not in a high-frequency fluctuation cycle at the current moment, the monitoring frequency of the meteorological data is determined to be a fixed preset frequency, and the power supply mode is adjusted according to the meteorological data.

2. The intelligent photovoltaic-storage-charging-detection energy collaborative optimization control method according to claim 1, characterized in that, The step of collecting historical information on photovoltaic power generation in charging stations and predicting the high-frequency fluctuation period of each charging station based on the historical information includes: Historical information on photovoltaic power generation in charging stations is collected, and the historical information on photovoltaic power generation in charging stations is used to train a neural network model to obtain a fluctuation prediction model; The fluctuation information of photovoltaic power generation in each charging station within each cycle is predicted based on the fluctuation prediction model. The fluctuation information includes the number of fluctuations of photovoltaic power generation in each charging station and the average duration of each fluctuation. Based on the number of fluctuations in the fluctuation information and the preset fluctuation threshold, it is determined whether the photovoltaic power generation of each power station is a high-frequency fluctuation cycle in each period.

3. The intelligent photovoltaic-storage-charging-detection energy collaborative optimization control method according to claim 1, characterized in that, When the photovoltaic power generation in the charging station is in a high-frequency fluctuation cycle at the current moment, the monitoring frequency of meteorological data is determined according to the average duration of each fluctuation predicted by the fluctuation prediction model, and the power supply mode is adjusted according to the meteorological data. The power supply mode includes a first power supply mode and a second power supply mode. When the photovoltaic power generation in the charging station is in a high-frequency fluctuation cycle at the current moment, the monitoring frequency of meteorological data is determined according to the average duration of each fluctuation predicted by the fluctuation prediction model. When an anomaly is detected in the meteorological data, the power supply mode is switched to the second power supply mode, which continues until the anomaly ends. The power supply mode will be restored from the second power supply mode to the first power supply mode within minutes. The value of is determined based on the number of fluctuations within the current high-frequency fluctuation cycle predicted by the fluctuation prediction model. The first power supply mode is: powering electric vehicles mainly through photovoltaic power generation and supplemented by energy storage system. If there is a surplus of electricity generated by photovoltaic power generation after meeting the power supply needs of electric vehicles, the surplus electricity generated by photovoltaic power generation will be stored in the energy storage system. The second power supply mode is: power is supplied to electric vehicles by a combination of energy storage system as the main source and grid as the auxiliary source. In this mode, photovoltaic power generation does not supply power to electric vehicles, but stores electrical energy through the energy storage system.

4. The intelligent photovoltaic energy storage charging and inspection coordinated optimization control method according to claim 3, characterized in that, When the photovoltaic power generation in the charging station is not in a high-frequency fluctuation cycle at the current moment, the step of determining that the monitoring frequency of the meteorological data is a fixed preset frequency and adjusting the power supply mode according to the meteorological data includes: When the photovoltaic power generation in the charging station is not in a high-frequency fluctuation cycle, the monitoring frequency of meteorological data is determined to be a fixed preset frequency. When an anomaly is detected in the meteorological data, the power supply mode is switched to the second power supply mode, which continues until the anomaly ends. The power supply mode will be restored from the second power supply mode to the first power supply mode within minutes.

5. The intelligent photovoltaic-storage-charging-detection energy collaborative optimization control method according to claim 3, characterized in that, Determining whether meteorological data is abnormal includes the following steps: collecting meteorological data and preprocessing it; comparing the preprocessed meteorological data with the set abnormal conditions; if the abnormal conditions are met, the meteorological data is determined to be abnormal, and the power supply mode is adjusted.

6. A smart photovoltaic-storage-charging-inspection energy collaborative optimization control system, characterized in that, The system includes: The high-frequency prediction module is used to collect historical information on photovoltaic power generation in the charging station and predict the high-frequency fluctuation period of each charging station based on the historical information. The high-frequency adjustment module is used to determine the monitoring frequency of meteorological data based on the average duration of each fluctuation predicted by the fluctuation prediction model when the photovoltaic power generation in the charging station is in a high-frequency fluctuation cycle at the current moment, and to adjust the power supply mode according to the meteorological data. The power supply mode includes a first power supply mode and a second power supply mode. Here, "fluctuation" means that the output power of photovoltaic power generation does not meet the preset output power and the fluctuation duration exceeds the preset duration, which can be counted as a fluctuation. The low-frequency adjustment module is used to determine that the monitoring frequency of meteorological data is a fixed preset frequency when the photovoltaic power generation in the charging station is not in a high-frequency fluctuation cycle at the current moment, and adjust the power supply mode according to the meteorological data.

7. The intelligent photovoltaic-storage-charging-detection energy collaborative optimization control system according to claim 6, characterized in that, The high-frequency prediction module includes: The prediction model building unit is used to collect historical information on photovoltaic power generation in the charging station, and to train the neural network model using the historical information on photovoltaic power generation in the charging station to obtain the fluctuation prediction model. The fluctuation information prediction unit is used to predict the fluctuation information of photovoltaic power generation in each charging station within each cycle according to the fluctuation prediction model. The fluctuation information includes the number of fluctuations of photovoltaic power generation in each charging station and the average duration of each fluctuation. The high-frequency cycle determination unit is used to determine whether the photovoltaic power generation of each power station is a high-frequency fluctuation cycle in each cycle based on the number of fluctuations in the fluctuation information and the preset fluctuation threshold.

8. The intelligent photovoltaic-storage-charging-detection energy collaborative optimization control system according to claim 6, characterized in that, The high-frequency adjustment module includes: The first monitoring frequency determination unit is used to determine the monitoring frequency of meteorological data based on the average duration of each fluctuation predicted by the fluctuation prediction model when the photovoltaic power generation in the charging station is in a high-frequency fluctuation cycle at the current moment. The first power supply mode switching unit is used to switch the power supply mode to the second power supply mode when an anomaly is detected in the meteorological data, until the anomaly ends. The power supply mode will be restored from the second power supply mode to the first power supply mode within minutes. The value of is determined based on the number of fluctuations within the current high-frequency fluctuation cycle predicted by the fluctuation prediction model. The first power supply mode is: powering electric vehicles mainly through photovoltaic power generation and supplemented by energy storage system. If there is a surplus of electricity generated by photovoltaic power generation after meeting the power supply needs of electric vehicles, the surplus electricity generated by photovoltaic power generation will be stored in the energy storage system. The second power supply mode is: power is supplied to electric vehicles by a combination of energy storage system as the main source and grid as the auxiliary source. In this mode, photovoltaic power generation does not supply power to electric vehicles, but stores electrical energy through the energy storage system.

9. The intelligent photovoltaic-storage-charging-detection energy collaborative optimization control system according to claim 8, characterized in that, The low-frequency adjustment module includes: The second monitoring frequency determination unit is used to determine that the monitoring frequency of meteorological data is a fixed preset frequency when the photovoltaic power generation in the charging station is not in a high-frequency fluctuation cycle. The second power supply mode switching unit is used to switch to the second power supply mode when an anomaly is detected in the meteorological data, until the anomaly ends. The power supply mode will be restored from the second power supply mode to the first power supply mode within minutes; The value of is determined based on the number of fluctuations in the current period predicted by the fluctuation prediction model.

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