Wind and light storage, charging and conversion comprehensive management and control system for local micro-grid and management and control method thereof

Abstract

The invention relates to an electric power resource management and control technology, and discloses a comprehensive management and control system for wind and light storage, charging and conversion of a local micro-grid, which comprises the following steps: calculating future wind power generation data according to pre-acquired wind power generation data and wind power generation historical data, future photovoltaic power generation data are predicted according to pre-acquired photovoltaic power generation data and photovoltaic power generation historical data, a future total power generation curve is obtained according to superposition of future wind power generation capacity and future photovoltaic power generation capacity, current power consumption data of the charging and replacing integrated power station are acquired, future power consumption demand data are predicted according to the current power consumption data, and the future power consumption demand data are acquired. Analyzing according to the future power consumption demand data, the future total generating capacity curve and the future power consumption demand curve to obtain a load index, and adaptively adjusting the power supply of the charging and replacing integrated power station according to the load index. The invention further provides a wind and light storage, charging and conversion comprehensive control method for the local micro-grid. The power resource utilization rate and stability of the local micro-grid can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power resource management and control, and in particular to a method, system, equipment and medium for comprehensive management and control of wind, solar, storage, charging and switching in a local microgrid. Background Art

[0002] As the world pays more and more attention to the energy crisis and environmental awareness continues to increase, the proportion of renewable energy in the energy field continues to rise. As the most widely used renewable energy, solar energy and wind energy have significant advantages such as cleanliness, pollution-free, and inexhaustible, and have become the main force in promoting energy transformation. Local microgrids, as an effective form of integrating renewable energy and realizing efficient use of distributed energy, have emerged and developed rapidly.

[0003] In the local microgrid architecture, each link of wind, solar, storage, charging and conversion is closely connected, forming a complex and organic whole. Wind power generation devices use the kinetic energy of natural wind to convert it into electrical energy, and solar photovoltaic panels capture solar radiation energy and convert it into electrical energy. The two together serve as the main power source of the microgrid, providing clean energy support for regional electricity demand. However, due to the inherent characteristics of wind and solar energy, their power generation is highly intermittent and volatile. Factors such as wind force, changes in wind direction, sunshine intensity, and cloud cover can cause the power generation to fluctuate greatly in a short period of time, posing severe challenges to the stable power supply of the microgrid.

[0004] The role of energy storage systems in microgrids is becoming increasingly critical. They can store excess electricity when there is sufficient wind and solar power generation, and release electricity when there is insufficient power generation or when electricity consumption is at peak, effectively smoothing power fluctuations and ensuring the stability of power supply in microgrids. However, at present, the management of energy storage systems faces many challenges. The capacity configuration of energy storage systems and the determination of charging and discharging power lack scientific and reasonable basis, which often leads to waste of resources or failure to meet actual needs. Summary of the invention

[0005] The present invention provides a system and method for comprehensive management and control of wind, solar, storage, charging and switching in a local microgrid, the main purpose of which is to solve the problem of low resource utilization in the existing comprehensive management and control system of wind, solar, storage, charging and switching in a local microgrid.

[0006] To achieve the above-mentioned purpose, the present invention provides a comprehensive management and control system for wind-solar-storage-charging-switch in a local microgrid, characterized in that the system includes a data acquisition module, a data prediction module, a curve generation module, a load analysis module and an adaptive adjustment module, wherein: The data acquisition module is used to acquire wind power generation data of the wind turbine and photovoltaic power generation data of the photovoltaic panel; The data prediction module is used to obtain historical wind power generation data, predict future wind power generation data based on the wind power generation data and the historical wind power generation data, obtain historical photovoltaic power generation data, and predict future photovoltaic power generation data based on the photovoltaic power generation data and the historical photovoltaic power generation data; The curve generation module is used to superimpose the future wind power generation data and the future photovoltaic power generation data to obtain future total power generation data, draw a future total power generation curve based on the future total power generation data, obtain current power consumption data of a preset integrated charging and swapping power station, predict future power demand data based on the current power consumption data, and draw a future power demand curve based on the future power demand data; The load analysis module is used to perform load analysis according to the future total power generation curve and the future power demand curve to obtain a load index; The adaptive adjustment module is used to adaptively adjust the power supply of the integrated charging and swapping power station according to the load index.

[0007] Optionally, when acquiring the wind power generation data of the wind turbine, the data acquisition module is specifically used to: Acquiring initial wind power generation data of the wind turbine; Calculating the noise index of the power generation data at each time point in the initial wind power generation data to obtain a noise index sequence; Determine the noise data in the initial wind power generation data according to a preset noise index threshold and the noise index sequence to obtain noise position data; Calculate the mean of two adjacent power generation data before and after each noise data based on the noise position data to obtain a mean value set; The noise data in the initial wind power generation data is replaced with the data in the mean value set based on the noise position data to obtain the wind power generation data.

[0008] Optionally, when the data acquisition module calculates the noise index of the power generation data at each time point in the initial wind power generation data to obtain the noise index sequence, it is specifically used to: The noise index is calculated using the following formula: ; in, is the number of the noise index sequence The noise index, is the initial wind power generation data Power generation data.

[0009] Optionally, when predicting future wind power generation data based on the wind power generation data and the wind power generation historical data, the data prediction module is specifically used to: The future wind power generation data is calculated based on the wind power generation data and the wind power generation historical data using the following formula: ; in, Indicates the future wind power generation data Power generation data, Indicates the wind power generation history data Power generation data, is the number of time points in the wind power generation data, The wind power generation data is Power generation data, is the base of natural logarithms.

[0010] Optionally, when predicting future electricity demand data based on the current electricity consumption data, the curve generation module is specifically used to: Obtaining historical electricity consumption data of the integrated charging and swapping power station; The following formula is used to predict future electricity demand data based on the historical electricity consumption data and the current electricity consumption data: ; in, The future electricity demand data Electricity consumption data, Indicates the historical electricity consumption data Electricity consumption data, is the number of time points in the current electricity consumption data, is the current power consumption data Electricity consumption data, is the base of natural logarithms.

[0011] Optionally, when the load analysis module performs load analysis according to the future total power generation curve and the future power demand curve to obtain the load index, it is specifically used to: Comparing the data sizes of the future total power generation curve and the future power demand curve at the same time point to obtain a comparison result; According to the comparison result, at the same time point, the time point at which the future total power generation curve data is less than the future power demand curve data is recorded as 1, and the time point at which the future total power generation curve data is greater than or equal to the future power demand curve data is recorded as 0, to obtain a power consumption data sequence; The load index is calculated according to the future total power generation curve, the future power demand curve and the power consumption data sequence.

[0012] Optionally, when the load analysis module calculates the load index according to the future total power generation curve, the future power demand curve and the power consumption data sequence, it is specifically used to: The load index is calculated using the following formula: ; in, is the load index, is the number of time points in the future total power generation curve, Indicates the total future power generation curve The amount of electricity generated at a point in time, Indicates the future electricity demand curve The power consumption at a point in time, is the preset weight factor, represents the first time point of the future total power generation curve, represents the last time point in the future total power generation curve, represents the integral of the total future electricity generation curve, Indicates the first The value at a time point, is the continuous time penalty weight factor.

[0013] Optionally, when the adaptive adjustment module adaptively adjusts the power supply of the integrated charging and swapping power station according to the load index, it is specifically used to: Calculating the power shortage power according to the load index; Determining whether the load index is greater than or equal to a preset load threshold; If the load index is less than the load threshold, a preset power storage device is used to store power according to the power gap power; If the load index is greater than or equal to the load threshold, a preset power storage device is used to supply power to the integrated charging and swapping power station according to the power gap power.

[0014] Optionally, when calculating the power gap power according to the load index, the adaptive adjustment module is specifically used to: The power gap power is calculated using the following formula: ; in, is the power gap power, is the load index, is the preset load index weight parameter, is the number of time points in the future total power generation curve, is the number of time points in the current electricity consumption data, Indicates the total future power generation curve The amount of electricity generated at a point in time, Indicates the future electricity demand curve The power consumption at a point in time.

[0015] In order to solve the above problems, an embodiment of the present invention provides a comprehensive management and control method for wind-solar-storage-charging conversion in a local microgrid, including: Obtain wind power generation data from wind turbines and photovoltaic power generation data from photovoltaic panels; Acquire historical wind power generation data, and predict future wind power generation data based on the wind power generation data and the historical wind power generation data; Acquire photovoltaic power generation historical data, and predict future photovoltaic power generation data based on the photovoltaic power generation data and the photovoltaic power generation historical data; Superimposing the future wind power generation data and the future photovoltaic power generation data to obtain future total power generation data, and drawing a future total power generation curve according to the future total power generation data; Obtaining current power consumption data of a preset integrated charging and swapping power station, predicting future power demand data based on the current power consumption data, and drawing a future power demand curve based on the future power demand data; Perform load analysis according to the future total power generation curve and the future power demand curve to obtain a load index; The power supply of the integrated charging and swapping power station is adaptively adjusted according to the load index.

[0016] The embodiment of the present invention obtains wind power generation data of a wind turbine, obtains photovoltaic power generation data of a photovoltaic panel, obtains historical wind power generation data, predicts future wind power generation data based on the wind power generation data and the historical wind power generation data, obtains historical photovoltaic power generation data, predicts future photovoltaic power generation data based on the photovoltaic power generation data and the historical photovoltaic power generation data, superimposes the future wind power generation data with the future photovoltaic power generation data to obtain future total power generation data, draws a future total power generation curve based on the future total power generation data, obtains current power consumption data of a preset integrated charging and swapping power station, predicts future power demand data based on the current power consumption data, draws a future power demand curve based on the future power demand data, performs load analysis based on the future total power generation curve and the future power demand curve to obtain a load index, and adaptively adjusts the power supply of the integrated charging and swapping power station based on the load index. It can improve the utilization rate of power resources and ensure the normal operation of local microgrids. Therefore, the comprehensive management and control system and method for wind, solar, storage, charging and switching of local microgrids proposed in the present invention can solve the problem of low resource utilization rate of existing comprehensive management and control systems for wind, solar, storage, charging and switching of local microgrids. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A functional module diagram of a comprehensive management and control system for wind, solar, storage, charging and switching in a local microgrid provided by an embodiment of the present invention; Figure 2 A flow chart of a method for integrated wind-solar-storage-charging-switch management and control in a local microgrid provided by an embodiment of the present invention; The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0018] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0019] The embodiment of the present application provides a method for comprehensive control of wind, solar, storage, charging and swapping for a local microgrid. The execution subject of the method for comprehensive control of wind, solar, storage, charging and swapping for a local microgrid includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the present application. In other words, the method for comprehensive control of wind, solar, storage, charging and swapping for a local microgrid can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc. The server can be an independent server, or it can be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and big data and artificial intelligence platforms.

[0020] like Figure 1 As shown, it is a functional module diagram of a comprehensive management and control system for wind, solar, storage, charging and switching in a local microgrid provided by an embodiment of the present invention.

[0021] The integrated control system 100 for wind, solar, storage, charging and switching of local microgrids described in the present invention can be installed in an electronic device. According to the functions to be implemented, the integrated control system 100 for wind, solar, storage, charging and switching of local microgrids can include a data acquisition module 101, a data prediction module 102, a curve generation module 103, a load analysis module 104 and an adaptive adjustment module 105. The module described in the present invention can also be referred to as a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0022] In this embodiment, the functions of each module / unit are as follows: The data acquisition module 101 is used to acquire wind power generation data of the wind turbine and photovoltaic power generation data of the photovoltaic panel; The data prediction module 102 is used to obtain historical wind power generation data, predict future wind power generation data based on the wind power generation data and the historical wind power generation data, obtain historical photovoltaic power generation data, and predict future photovoltaic power generation data based on the photovoltaic power generation data and the historical photovoltaic power generation data; The curve generating module 103 is used to superimpose the future wind power generation data and the future photovoltaic power generation data to obtain future total power generation data, draw a future total power generation curve based on the future total power generation data, obtain current power consumption data of a preset integrated charging and swapping power station, predict future power demand data based on the current power consumption data, and draw a future power demand curve based on the future power demand data; The load analysis module 104 is used to perform load analysis according to the future total power generation curve and the future power demand curve to obtain a load index; The adaptive adjustment module 105 is used to adaptively adjust the power supply of the integrated charging and swapping power station according to the load index.

[0023] The data acquisition module 101 is used to acquire wind power generation data of a wind turbine and photovoltaic power generation data of a photovoltaic panel.

[0024] In the embodiment of the present invention, the obtaining of wind power generation data of the wind turbine and the obtaining of photovoltaic power generation data of the photovoltaic panel may be obtaining wind power generation data using sensors preset in the wind turbine and obtaining photovoltaic power generation data using sensors preset in the photovoltaic panel.

[0025] In the embodiment of the present invention, the sensor includes a current sensor and a voltage sensor.

[0026] In the embodiment of the present invention, the wind power generation data of the wind turbine is acquired according to a preset acquisition frequency, and the photovoltaic power generation data is also acquired according to the acquisition frequency.

[0027] In detail, the preset collection frequency may be once per minute.

[0028] In the embodiment of the present invention, when acquiring the wind power generation data of the wind turbine, the data acquisition module 101 is specifically used to: Acquiring initial wind power generation data of the wind turbine; Calculating the noise index of the power generation data at each time point in the initial wind power generation data to obtain a noise index sequence; Determine the noise data in the initial wind power generation data according to a preset noise index threshold and the noise index sequence to obtain noise position data; Calculate the mean of two adjacent power generation data before and after each noise data based on the noise position data to obtain a mean value set; The noise data in the initial wind power generation data is replaced with the data in the mean value set based on the noise position data to obtain the wind power generation data.

[0029] In detail, the initial wind power generation data is a data set containing noisy data.

[0030] In detail, the noise index threshold is a threshold set according to task requirements. The lower the noise index threshold, the smoother the wind power generation data obtained by subsequent processing. The higher the noise index threshold, the closer the wind power generation data obtained by subsequent processing is to the original data, but inconspicuous noise may be omitted.

[0031] In detail, the noise position data is a data set containing the sequential positions of the noise data in the initial wind power generation data.

[0032] In detail, when the data acquisition module 101 calculates the noise index of the power generation data at each time point in the initial wind power generation data to obtain the noise index sequence, it is specifically used to: The noise index is calculated using the following formula: ; in, is the number of the noise index sequence The noise index, is the initial wind power generation data Power generation data.

[0033] In the embodiment of the present invention, by acquiring wind power generation data of a wind turbine and photovoltaic power generation data of a photovoltaic panel, the efficiency of subsequent prediction of future wind power generation data and future photovoltaic power generation data can be improved.

[0034] The data prediction module 102 is used to obtain historical wind power generation data, predict future wind power generation data based on the wind power generation data and the historical wind power generation data, obtain historical photovoltaic power generation data, and predict future photovoltaic power generation data based on the photovoltaic power generation data and the historical photovoltaic power generation data.

[0035] In the embodiment of the present invention, the wind power generation history data may be the wind power generation data of the wind turbine in the last 24 hours.

[0036] In an embodiment of the present invention, predicting future wind power generation data based on the wind power generation data and the wind power generation historical data is performed by analyzing the difference between the current wind power generation data and the power generation data of the wind power generation historical data in the same time period of a day, and then predicting the future wind power generation data based on the analyzed difference.

[0037] In the embodiment of the present invention, when predicting future wind power generation data based on the wind power generation data and the wind power generation historical data, the data prediction module 102 is specifically used to: The future wind power generation data is calculated based on the wind power generation data and the wind power generation historical data using the following formula: ; in, Indicates the future wind power generation data Power generation data, Indicates the wind power generation history data Power generation data, is the number of time points in the wind power generation data, The wind power generation data is Power generation data, is the base of natural logarithms.

[0038] In the embodiment of the present invention, by acquiring historical wind power generation data and predicting future wind power generation data based on the wind power generation data and the historical wind power generation data, the accuracy of the prediction of the future wind power generation data can be improved.

[0039] In the embodiment of the present invention, the photovoltaic power generation history data may be power generation data of the photovoltaic panel in the last 24 hours.

[0040] In the embodiment of the present invention, the method for predicting future photovoltaic power generation data based on the photovoltaic power generation data and the photovoltaic power generation historical data is the same as the method for predicting future wind power generation data based on the wind power generation data and the wind power generation historical data, which will not be repeated here.

[0041] In the embodiment of the present invention, by acquiring historical photovoltaic power generation data and predicting future photovoltaic power generation data based on the photovoltaic power generation data and the historical photovoltaic power generation data, the prediction accuracy of the future photovoltaic power generation data can be improved.

[0042] The curve generating module 103 is used to superimpose the future wind power generation data with the future photovoltaic power generation data to obtain future total power generation data, draw a future total power generation curve based on the future total power generation data, obtain the current power consumption data of the preset integrated charging and swapping power station, predict the future power demand data based on the current power consumption data, and draw a future power demand curve based on the future power demand data.

[0043] In the embodiment of the present invention, superimposing the future wind power generation data with the future photovoltaic power generation data to obtain future total power generation data means adding the future wind power generation and the future photovoltaic power generation at the same time point to obtain the future total power generation data.

[0044] In the embodiment of the present invention, drawing the future total power generation curve according to the future total power generation data refers to inputting the future total power generation data into a coordinate system with time as the horizontal coordinate and power generation as the vertical coordinate based on a time series to form a curve.

[0045] In an embodiment of the present invention, by superimposing the future wind power generation data with the future photovoltaic power generation data to obtain future total power generation data, and drawing a future total power generation curve based on the future total power generation data, the efficiency and accuracy of subsequent load analysis can be improved.

[0046] In the embodiment of the present invention, the integrated charging and swapping power station is a comprehensive energy supply facility integrating multiple functions such as charging, battery swapping, and energy storage, aiming to meet the rapid energy replenishment needs of new energy vehicles while optimizing energy utilization efficiency.

[0047] In the embodiment of the present invention, the obtaining of the current power consumption data of the preset integrated charging and swapping station refers to obtaining the power consumption data of the integrated charging and swapping station from 0:00 on the day to the current time.

[0048] In the embodiment of the present invention, when predicting future electricity demand data according to the current electricity consumption data, the curve generation module 103 is specifically used to: Obtaining historical electricity consumption data of the integrated charging and swapping power station; The following formula is used to predict future electricity demand data based on the historical electricity consumption data and the current electricity consumption data: ; in, The future electricity demand data Electricity consumption data, Indicates the historical electricity consumption data Electricity consumption data, is the number of time points in the current electricity consumption data, is the current power consumption data Electricity consumption data, is the base of natural logarithms.

[0049] In an embodiment of the present invention, by obtaining the current power consumption data of a preset integrated charging and swapping power station, predicting future power demand data based on the current power consumption data, and drawing a future power demand curve based on the future power demand data, the efficiency and accuracy of subsequent load index analysis can be improved.

[0050] The load analysis module 104 is used to perform load analysis according to the future total power generation curve and the future power demand curve to obtain a load index.

[0051] In the embodiment of the present invention, when the load analysis module 104 performs load analysis according to the future total power generation curve and the future power demand curve to obtain the load index, it is specifically used to: Comparing the data sizes of the future total power generation curve and the future power demand curve at the same time point to obtain a comparison result; According to the comparison result, at the same time point, the time point at which the future total power generation curve data is less than the future power demand curve data is recorded as 1, and the time point at which the future total power generation curve data is greater than or equal to the future power demand curve data is recorded as 0, to obtain a power consumption data sequence; The load index is calculated according to the future total power generation curve, the future power demand curve and the power consumption data sequence.

[0052] In the embodiment of the present invention, when the load analysis module 104 calculates the load index according to the future total power generation curve, the future power demand curve and the power consumption data sequence, it is specifically used to: The load index is calculated using the following formula: ; in, is the load index, is the number of time points in the future total power generation curve, Indicates the total future power generation curve The amount of electricity generated at a point in time, Indicates the future electricity demand curve The power consumption at a point in time, is the preset weight factor, represents the first time point of the future total power generation curve, represents the last time point in the future total power generation curve, represents the integral of the total future electricity generation curve, Indicates the first The value at a time point, is the continuous time penalty weight factor.

[0053] In the embodiment of the present invention, the power consumption data sequence is a data sequence containing only 1 and 0, which can be used to determine the duration of time that the future total power generation curve data is continuously less than the future power demand curve data. In the process of calculating the load index, the longer the future total power generation curve data is continuously less than the future power demand curve data, the higher the load index.

[0054] In an embodiment of the present invention, a load index is obtained by performing load analysis based on the future total power generation curve and the future electricity demand curve, so that different adjustment measurements can be performed according to the size of the load index, thereby improving the safety of the integrated charging and swapping power station.

[0055] The adaptive adjustment module 105 is used to adaptively adjust the power supply of the integrated charging and swapping power station according to the load index.

[0056] In the embodiment of the present invention, when the adaptive adjustment module 105 adaptively adjusts the power supply of the charging and swapping integrated power station according to the load index, it is specifically used to: Calculating the power shortage power according to the load index; Determining whether the load index is greater than or equal to a preset load threshold; If the load index is less than the load threshold, a preset power storage device is used to store power according to the power gap power; If the load index is greater than or equal to the load threshold, a preset power storage device is used to supply power to the integrated charging and swapping power station according to the power gap power.

[0057] In the embodiment of the present invention, when the adaptive adjustment module 105 calculates the power shortage power according to the load index, it is specifically used to: The power gap power is calculated using the following formula: ; in, is the power gap power, is the load index, is the preset load index weight parameter, is the number of time points in the future total power generation curve, is the number of time points in the current electricity consumption data, Indicates the total future power generation curve The amount of electricity generated at a point in time, Indicates the future electricity demand curve The power consumption at a point in time.

[0058] In the embodiment of the present invention, when the load index is less than the load threshold, the utilization rate of resources can be improved by using a preset power storage device to store power according to the power gap power.

[0059] In an embodiment of the present invention, when the load index is greater than or equal to the load threshold, the power supply efficiency of the integrated charging and swapping station can be guaranteed by utilizing a preset power storage device to supply power to the integrated charging and swapping station according to the power gap power.

[0060] In detail, each module described in the integrated management and control system 100 for wind, solar, storage and charging conversion of a local microgrid described in the embodiment of the present invention is used in the same manner as described above. Figure 2 The same technical means are used for the comprehensive control method of wind, solar, storage and charging for local microgrids described in the text, and can produce the same technical effects, so they will not be repeated here.

[0061] Reference Figure 2 As shown, it is a flow chart of a method for comprehensive control of wind, solar, storage, charging and switching in a local microgrid provided by an embodiment of the present invention. In this embodiment, the method for comprehensive control of wind, solar, storage, charging and switching in a local microgrid includes: S1. Obtain wind power generation data of a wind turbine and obtain photovoltaic power generation data of a photovoltaic panel.

[0062] In the embodiment of the present invention, the obtaining of wind power generation data of the wind turbine and the obtaining of photovoltaic power generation data of the photovoltaic panel may be obtaining wind power generation data using sensors preset in the wind turbine and obtaining photovoltaic power generation data using sensors preset in the photovoltaic panel.

[0063] In the embodiment of the present invention, the sensor includes a current sensor and a voltage sensor.

[0064] In the embodiment of the present invention, the wind power generation data of the wind turbine is acquired according to a preset acquisition frequency, and the photovoltaic power generation data is also acquired according to the acquisition frequency.

[0065] In detail, the preset collection frequency may be once per minute.

[0066] In the embodiment of the present invention, the step of obtaining wind power generation data of a wind turbine includes: Acquiring initial wind power generation data of the wind turbine; Calculating the noise index of the power generation data at each time point in the initial wind power generation data to obtain a noise index sequence; Determine the noise data in the initial wind power generation data according to a preset noise index threshold and the noise index sequence to obtain noise position data; Calculate the mean of two adjacent power generation data before and after each noise data based on the noise position data to obtain a mean value set; The noise data in the initial wind power generation data is replaced with the data in the mean value set based on the noise position data to obtain the wind power generation data.

[0067] In detail, the initial wind power generation data is a data set containing noisy data.

[0068] In detail, the noise index threshold is a threshold set according to task requirements. The lower the noise index threshold, the smoother the wind power generation data obtained by subsequent processing. The higher the noise index threshold, the closer the wind power generation data obtained by subsequent processing is to the original data, but inconspicuous noise may be omitted.

[0069] In detail, the noise position data is a data set containing the sequential positions of the noise data in the initial wind power generation data.

[0070] In detail, the step of calculating the noise index of the power generation data at each time point in the initial wind power generation data to obtain a noise index sequence includes: The noise index is calculated using the following formula: ; in, is the number of the noise index sequence The noise index, is the initial wind power generation data Power generation data.

[0071] In the embodiment of the present invention, by acquiring wind power generation data of a wind turbine and photovoltaic power generation data of a photovoltaic panel, the efficiency of subsequent prediction of future wind power generation data and future photovoltaic power generation data can be improved.

[0072] S2. Acquire historical wind power generation data, and predict future wind power generation data based on the wind power generation data and the historical wind power generation data.

[0073] In the embodiment of the present invention, the wind power generation history data may be the wind power generation data of the wind turbine in the last 24 hours.

[0074] In an embodiment of the present invention, predicting future wind power generation data based on the wind power generation data and the wind power generation historical data is performed by analyzing the difference between the current wind power generation data and the power generation data of the wind power generation historical data in the same time period of a day, and then predicting the future wind power generation data based on the analyzed difference.

[0075] In an embodiment of the present invention, the method of predicting future wind power generation data based on the wind power generation data and the wind power generation historical data includes: The future wind power generation data is calculated based on the wind power generation data and the wind power generation historical data using the following formula: ; in, Indicates the future wind power generation data Power generation data, Indicates the wind power generation history data Power generation data, is the number of time points in the wind power generation data, The wind power generation data is Power generation data, is the base of natural logarithms.

[0076] In the embodiment of the present invention, by acquiring historical wind power generation data and predicting future wind power generation data based on the wind power generation data and the historical wind power generation data, the accuracy of the prediction of the future wind power generation data can be improved.

[0077] S3. Acquire historical photovoltaic power generation data, and predict future photovoltaic power generation data based on the photovoltaic power generation data and the historical photovoltaic power generation data.

[0078] In the embodiment of the present invention, the photovoltaic power generation history data may be power generation data of the photovoltaic panel in the last 24 hours.

[0079] In the embodiment of the present invention, the method for predicting future photovoltaic power generation data based on the photovoltaic power generation data and the photovoltaic power generation historical data is the same as the method for predicting future wind power generation data based on the wind power generation data and the wind power generation historical data, which will not be repeated here.

[0080] In the embodiment of the present invention, by acquiring historical photovoltaic power generation data and predicting future photovoltaic power generation data based on the photovoltaic power generation data and the historical photovoltaic power generation data, the prediction accuracy of the future photovoltaic power generation data can be improved.

[0081] S4. Superimposing the future wind power generation data and the future photovoltaic power generation data to obtain future total power generation data, and drawing a future total power generation curve according to the future total power generation data.

[0082] In the embodiment of the present invention, superimposing the future wind power generation data with the future photovoltaic power generation data to obtain future total power generation data means adding the future wind power generation and the future photovoltaic power generation at the same time point to obtain the future total power generation data.

[0083] In the embodiment of the present invention, drawing the future total power generation curve according to the future total power generation data refers to inputting the future total power generation data into a coordinate system with time as the horizontal coordinate and power generation as the vertical coordinate based on a time series to form a curve.

[0084] In an embodiment of the present invention, by superimposing the future wind power generation data with the future photovoltaic power generation data to obtain future total power generation data, and drawing a future total power generation curve based on the future total power generation data, the efficiency and accuracy of subsequent load analysis can be improved.

[0085] S5. Obtain current power consumption data of a preset integrated charging and swapping power station, predict future power demand data based on the current power consumption data, and draw a future power demand curve based on the future power demand data.

[0086] In the embodiment of the present invention, the integrated charging and swapping power station is a comprehensive energy supply facility integrating multiple functions such as charging, battery swapping, and energy storage, aiming to meet the rapid energy replenishment needs of new energy vehicles while optimizing energy utilization efficiency.

[0087] In the embodiment of the present invention, the predicting of future electricity demand data based on the current electricity consumption data includes: Obtaining historical electricity consumption data of the integrated charging and swapping power station; The following formula is used to predict future electricity demand data based on the historical electricity consumption data and the current electricity consumption data: ; in, The future electricity demand data Electricity consumption data, Indicates the historical electricity consumption data Electricity consumption data, is the number of time points in the current electricity consumption data, is the current power consumption data Electricity consumption data, is the base of natural logarithms.

[0088] In an embodiment of the present invention, by obtaining the current power consumption data of a preset integrated charging and swapping power station, predicting future power demand data based on the current power consumption data, and drawing a future power demand curve based on the future power demand data, the efficiency and accuracy of subsequent load index analysis can be improved.

[0089] S6. Perform load analysis according to the future total power generation curve and the future power demand curve to obtain a load index.

[0090] In the embodiment of the present invention, the load analysis is performed according to the future total power generation curve and the future power demand curve to obtain the load index, including: Comparing the data sizes of the future total power generation curve and the future power demand curve at the same time point to obtain a comparison result; According to the comparison result, at the same time point, the time point at which the future total power generation curve data is less than the future power demand curve data is recorded as 1, and the time point at which the future total power generation curve data is greater than or equal to the future power demand curve data is recorded as 0, to obtain a power consumption data sequence; The load index is calculated according to the future total power generation curve, the future power demand curve and the power consumption data sequence.

[0091] In the embodiment of the present invention, the step of calculating the load index according to the future total power generation curve, the future power demand curve and the power consumption data sequence includes: The load index is calculated using the following formula: ; in, is the load index, is the number of time points in the future total power generation curve, Indicates the total future power generation curve The amount of electricity generated at a point in time, Indicates the future electricity demand curve The power consumption at a point in time, is the preset weight factor, represents the first time point of the future total power generation curve, represents the last time point in the future total power generation curve, represents the integral of the total future electricity generation curve, Indicates the first The value at a time point, is the continuous time penalty weight factor.

[0092] In the embodiment of the present invention, the power consumption data sequence is a data sequence containing only 1 and 0, which can be used to determine the duration of time that the future total power generation curve data is continuously less than the future power demand curve data. In the process of calculating the load index, the longer the future total power generation curve data is continuously less than the future power demand curve data, the higher the load index.

[0093] In an embodiment of the present invention, a load index is obtained by performing load analysis based on the future total power generation curve and the future electricity demand curve, so that different adjustment measurements can be performed according to the size of the load index, thereby improving the safety of the integrated charging and swapping power station.

[0094] S7. Adaptively adjust the power supply of the integrated charging and swapping power station according to the load index.

[0095] In an embodiment of the present invention, the adaptively adjusting the power supply of the charging and swapping integrated power station according to the load index includes: Calculating the power shortage power according to the load index; Determining whether the load index is greater than or equal to a preset load threshold; If the load index is less than the load threshold, a preset power storage device is used to store power according to the power gap power; If the load index is greater than or equal to the load threshold, a preset power storage device is used to supply power to the integrated charging and swapping power station according to the power gap power.

[0096] In the embodiment of the present invention, the calculating of the power shortage power according to the load index includes: The power gap power is calculated using the following formula: ; in, is the power gap power, is the load index, is the preset load index weight parameter, is the number of time points in the future total power generation curve, is the number of time points in the current electricity consumption data, Indicates the total future power generation curve The amount of electricity generated at a point in time, Indicates the future electricity demand curve The power consumption at a point in time.

[0097] In the embodiment of the present invention, when the load index is less than the load threshold, the utilization rate of resources can be improved by using a preset power storage device to store power according to the power gap power.

[0098] In an embodiment of the present invention, when the load index is greater than or equal to the load threshold, the power supply efficiency of the integrated charging and swapping station can be guaranteed by utilizing a preset power storage device to supply power to the integrated charging and swapping station according to the power gap power.

[0099] In the embodiments provided by the present invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are only illustrative, for example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0100] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0101] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0102] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0103] Therefore, no matter from which point of view, the embodiments should be regarded as illustrative and non-restrictive, and the scope of the present invention is limited by the appended claims rather than the above description, so it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any attached figure mark in the claims should not be regarded as limiting the claims involved.

[0104] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Artificial Intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0105] In addition, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in the system claim can also be implemented by one unit or device through software or hardware. The words first, second, etc. are used to indicate names, and do not indicate any particular order.

[0106] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A comprehensive management and control system for wind and solar storage and charging conversion in a local microgrid, characterized in that: The system includes a data acquisition module, a data prediction module, a curve generation module, a load analysis module and an adaptive adjustment module, wherein: The data acquisition module is used to acquire wind power generation data of the wind turbine and photovoltaic power generation data of the photovoltaic panel; The data prediction module is used to obtain historical wind power generation data, predict future wind power generation data based on the wind power generation data and the historical wind power generation data, obtain historical photovoltaic power generation data, and predict future photovoltaic power generation data based on the photovoltaic power generation data and the historical photovoltaic power generation data; The curve generation module is used to superimpose the future wind power generation data and the future photovoltaic power generation data to obtain future total power generation data, draw a future total power generation curve based on the future total power generation data, obtain current power consumption data of a preset integrated charging and swapping power station, predict future power demand data based on the current power consumption data, and draw a future power demand curve based on the future power demand data; The load analysis module is used to perform load analysis according to the future total power generation curve and the future power demand curve to obtain a load index; The adaptive adjustment module is used to adaptively adjust the power supply of the integrated charging and swapping power station according to the load index.

2. The integrated management and control system for wind-solar-storage-charging conversion for local microgrids as claimed in claim 1 is characterized in that: When acquiring the wind power generation data of the wind turbine, the data acquisition module is specifically used to: Acquiring initial wind power generation data of the wind turbine; Calculating the noise index of the power generation data at each time point in the initial wind power generation data to obtain a noise index sequence; Determine the noise data in the initial wind power generation data according to a preset noise index threshold and the noise index sequence to obtain noise position data; Calculate the mean of two adjacent power generation data before and after each noise data based on the noise position data to obtain a mean value set; The noise data in the initial wind power generation data is replaced with the data in the mean value set based on the noise position data to obtain the wind power generation data.

3. The integrated management and control system for wind-solar-storage-charging conversion for local microgrid as claimed in claim 2 is characterized in that: When the data acquisition module calculates the noise index of the power generation data at each time point in the initial wind power generation data to obtain the noise index sequence, it is specifically used to: The noise index is calculated using the following formula: ； in, is the first The noise index, is the first Power generation data.

4. The integrated management and control system for wind-solar-storage-charging conversion for local microgrid according to claim 1, characterized in that: When predicting future wind power generation data based on the wind power generation data and the wind power generation historical data, the data prediction module is specifically used to: The future wind power generation data is calculated based on the wind power generation data and the wind power generation historical data using the following formula: ； in, Indicates the future wind power generation data Power generation data, Indicates the wind power generation history data Power generation data, is the number of time points in the wind power generation data, The wind power generation data is Power generation data, is the base of natural logarithms.

5. The integrated management and control system for wind-solar-storage-charging conversion for local microgrid according to claim 1, characterized in that: When predicting future electricity demand data based on the current electricity consumption data, the curve generation module is specifically used to: Obtaining historical electricity consumption data of the integrated charging and swapping power station; The following formula is used to predict future electricity demand data based on the historical electricity consumption data and the current electricity consumption data: ； in, The future electricity demand data Electricity consumption data, Indicates the historical electricity consumption data Electricity consumption data, is the number of time points in the current electricity consumption data, is the current power consumption data Electricity consumption data, is the base of natural logarithms.

6. The integrated management and control system for wind-solar-storage-charging conversion for local microgrid according to claim 1, characterized in that: When the load analysis module performs load analysis according to the future total power generation curve and the future power demand curve to obtain the load index, it is specifically used to: Comparing the data sizes of the future total power generation curve and the future power demand curve at the same time point to obtain a comparison result; According to the comparison result, at the same time point, the time point at which the future total power generation curve data is less than the future power demand curve data is recorded as 1, and the time point at which the future total power generation curve data is greater than or equal to the future power demand curve data is recorded as 0, to obtain a power consumption data sequence; The load index is calculated according to the future total power generation curve, the future power demand curve and the power consumption data sequence.

7. The integrated management and control system for wind-solar-storage-charging conversion for local microgrid according to claim 1 is characterized in that: When the load analysis module calculates the load index according to the future total power generation curve, the future power demand curve and the power consumption data sequence, it is specifically used to: The load index is calculated using the following formula: ； in, is the load index, is the number of time points in the future total power generation curve, Indicates the total future power generation curve The amount of electricity generated at a point in time, Indicates the future electricity demand curve The power consumption at a point in time, is the preset weight factor, represents the first time point of the future total power generation curve, represents the last time point in the future total power generation curve, represents the integral of the total future electricity generation curve, Indicates the first The value at a time point, is the continuous time penalty weight factor.

8. The integrated management and control system for wind-solar-storage-charging conversion for local microgrids as claimed in claim 1 is characterized in that: When the adaptive adjustment module adaptively adjusts the power supply of the charging and swapping integrated power station according to the load index, it is specifically used to: Calculating the power shortage power according to the load index; Determining whether the load index is greater than or equal to a preset load threshold; If the load index is less than the load threshold, a preset power storage device is used to store power according to the power gap power; If the load index is greater than or equal to the load threshold, a preset power storage device is used to supply power to the integrated charging and swapping power station according to the power gap power.

9. The integrated management and control system for wind-solar-storage-charging conversion for local microgrid as claimed in claim 1, characterized in that: When the adaptive adjustment module calculates the power shortage power according to the load index, it is specifically used to: The power gap power is calculated using the following formula: ； in, is the power gap power, is the load index, is the preset load index weight parameter, is the number of time points in the future total power generation curve, is the number of time points in the current electricity consumption data, Indicates the total future power generation curve The amount of electricity generated at a point in time, Indicates the future electricity demand curve The power consumption at a point in time.

10. A comprehensive management and control method for wind-solar storage-charging conversion in a local microgrid, characterized in that: The method comprises: Obtain wind power generation data from wind turbines and photovoltaic power generation data from photovoltaic panels; Acquire historical wind power generation data, and predict future wind power generation data based on the wind power generation data and the historical wind power generation data; Acquire photovoltaic power generation historical data, and predict future photovoltaic power generation data based on the photovoltaic power generation data and the photovoltaic power generation historical data; Superimposing the future wind power generation data and the future photovoltaic power generation data to obtain future total power generation data, and drawing a future total power generation curve according to the future total power generation data; Obtaining current power consumption data of a preset integrated charging and swapping power station, predicting future power demand data based on the current power consumption data, and drawing a future power demand curve based on the future power demand data; Perform load analysis according to the future total power generation curve and the future power demand curve to obtain a load index; The power supply of the integrated charging and swapping power station is adaptively adjusted according to the load index.

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