New energy unit space-time characteristic analysis system and method based on map tool

Abstract

The invention provides a new energy unit time-space characteristic analysis system and method based on a map tool, and relates to the technical field of new energy data analysis, and the method comprises the steps: building a data mining platform, executing new energy data mining based on a spatial scale and a time scale, obtaining new energy spatial data and new energy time data, and obtaining new energy unit time-space characteristic data; and carrying out fluctuation and intermittency analysis to obtain new energy space characteristics and new energy time characteristics, carrying out complementarity analysis to obtain new energy complementation characteristics, and carrying out load planning of the new energy unit according to the new energy complementation characteristics. Through the method and the device, the technical problem of poor energy utilization efficiency caused by limitation to a single data source or analysis dimension due to lack of accurate analysis of space-time characteristics in existing new energy data analysis in the prior art can be solved, and the energy utilization efficiency is improved through integration of multi-source data and data mining based on a spatial scale and a time scale. The layout and operation strategy of the new energy unit are optimized, and the utilization efficiency of new energy is improved.

Description

Technical Field

[0001] The present application relates to the technical field of new energy data analysis, and in particular to a system and method for analyzing the spatiotemporal characteristics of new energy units based on map tools. Background Art

[0002] As the global demand for renewable energy continues to grow, the deployment and use of new energy units (such as wind turbines and solar power generation systems) are increasing. These new energy units play an important role in providing a clean and renewable source of electricity. However, the operation of new energy units is affected by many factors, including weather conditions, geographical location, and seasonal changes. When analyzing the spatiotemporal characteristics of new energy units, existing technologies are often limited to a single data source or analysis dimension, which limits the in-depth understanding of the volatility and intermittency of new energy units in space and time, thereby affecting the effective utilization and scheduling of new energy units. There are limitations in the load planning of new energy units, and the complementary characteristics of new energy cannot be fully utilized, resulting in the impact on the operating efficiency and economic benefits of the energy system.

[0003] In summary, the existing technology has the technical problem that the existing new energy data analysis lacks accurate analysis of temporal and spatial characteristics, which leads to being limited to a single data source or analysis dimension, resulting in poor energy utilization efficiency. Summary of the invention

[0004] The purpose of this application is to provide a system and method for analyzing the spatiotemporal characteristics of new energy units based on map tools, so as to solve the technical problem in the prior art that the existing new energy data analysis lacks accurate analysis of spatiotemporal characteristics, resulting in being limited to a single data source or analysis dimension, resulting in poor energy utilization efficiency.

[0005] In view of the above problems, the present application provides a system and method for analyzing the spatiotemporal characteristics of new energy units based on map tools.

[0006] In the first aspect, the present application provides a new energy unit spatiotemporal characteristics analysis system based on a map tool, wherein the new energy unit spatiotemporal characteristics analysis system based on a map tool includes: a data mining module, which is used to integrate a meteorological platform, an energy management platform, a remote sensing data platform and a geographic information system to establish a data mining platform, and perform new energy data mining based on spatial scale and time scale through the data mining platform to obtain new energy spatial data and new energy time data; a spatial characteristics analysis module, which is used to perform volatility and intermittency analysis on the new energy spatial data based on the spatial scale to obtain new energy spatial characteristics; a time characteristics analysis module, which is used to perform volatility and intermittency analysis on the new energy time data based on the time scale to obtain new energy time characteristics; a complementary analysis module, which is used to perform complementary analysis of the new energy spatial characteristics and the new energy time characteristics to obtain new energy complementary characteristics; a load planning module, which is used to perform load planning of new energy units based on the map tool platform according to the new energy complementary characteristics.

[0007] In the second aspect, the present application also provides a method for analyzing the spatiotemporal characteristics of new energy units based on map tools, wherein the method for analyzing the spatiotemporal characteristics of new energy units based on map tools includes: integrating a meteorological platform, an energy management platform, a remote sensing data platform and a geographic information system to establish a data mining platform, and performing new energy data mining based on spatial scale and time scale through the data mining platform to obtain new energy spatial data and new energy time data; performing volatility and intermittency analysis on the new energy spatial data based on the spatial scale to obtain new energy spatial characteristics; performing volatility and intermittency analysis on the new energy time data based on the time scale to obtain new energy time characteristics; performing complementarity analysis on the new energy spatial characteristics and the new energy time characteristics to obtain new energy complementary characteristics; and performing load planning for new energy units based on the map tool platform according to the new energy complementary characteristics.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0009] The data mining module is used to integrate the meteorological platform, energy management platform, remote sensing data platform and geographic information system to establish a data mining platform, and the data mining platform is used to perform new energy data mining based on spatial scale and time scale to obtain new energy spatial data and new energy time data; the spatial characteristic analysis module is used to perform volatility and intermittency analysis on the new energy spatial data based on the spatial scale to obtain new energy spatial characteristics; the time characteristic analysis module is used to perform volatility and intermittency analysis on the new energy time data based on the time scale to obtain new energy time characteristics; the complementary analysis module is used to perform complementary analysis on the new energy spatial characteristics and the new energy time characteristics to obtain new energy complementary characteristics; the load planning module is used to perform load planning of new energy units based on the map tool platform according to the new energy complementary characteristics. In other words, by integrating multi-source data and mining data based on spatial scale and time scale, the layout and operation strategy of new energy units are optimized to improve the utilization efficiency of new energy.

[0010] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented according to the contents of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically cited below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0012] Figure 1 A structural schematic diagram of a new energy unit spatiotemporal characteristic analysis system based on a map tool for this application;

[0013] Figure 2 This is a flow chart of a method for analyzing the spatiotemporal characteristics of a new energy unit based on a map tool.

[0014] Explanation of the reference numerals: data mining module 11 , spatial characteristic analysis module 12 , time characteristic analysis module 13 , complementary analysis module 14 , load planning module 15 . DETAILED DESCRIPTION

[0015] This application provides a system and method for analyzing the spatiotemporal characteristics of new energy units based on map tools, which solves the technical problem that the existing new energy data analysis lacks accurate analysis of spatiotemporal characteristics, resulting in poor energy utilization efficiency due to being limited to a single data source or analysis dimension. By integrating multi-source data and mining data based on spatial and temporal scales, the layout and operation strategy of new energy units are optimized to improve the utilization efficiency of new energy.

[0016] Below, the technical solutions in the present application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments of the present application. It should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application. It should also be noted that, for the convenience of description, only the parts related to the present application are shown in the accompanying drawings, rather than all of them.

[0017] For example, please refer to the attached Figure 1 The present application provides a new energy unit spatiotemporal characteristic analysis system based on a map tool, wherein the new energy unit spatiotemporal characteristic analysis system based on a map tool is used to implement the steps of a new energy unit spatiotemporal characteristic analysis method based on a map tool, and the new energy unit spatiotemporal characteristic analysis system based on a map tool includes:

[0018] The data mining module 11 is used to integrate the meteorological platform, energy management platform, remote sensing data platform and geographic information system to establish a data mining platform, and perform new energy data mining based on spatial scale and time scale through the data mining platform to obtain new energy spatial data and new energy time data.

[0019] Specifically, determine the goals and needs of data mining, and select appropriate meteorological platforms, energy management platforms, remote sensing data platforms, and GIS software. The meteorological platform provides real-time meteorological data, such as wind speed, wind direction, temperature, humidity, etc., which have an important impact on the power generation efficiency of new energy, especially wind and solar energy. The energy management platform provides operating data of new energy units, including power generation, operating status, etc. The remote sensing data platform provides surface coverage data, terrain data, etc., which have an important impact on the site selection and layout of new energy units. The geographic information system integrates these data into the map and provides spatial analysis functions. Through these platforms, a comprehensive data mining platform is established, which can collect and process new energy data from different sources, including weather conditions, energy consumption, remote sensing images, and geographic information. Through this data mining platform, new energy data mining based on spatial scale and time scale is performed. Data mining on the spatial scale reveals the stability and reliability of new energy power generation in different geographical locations, and data mining on the time scale reveals the changing laws of new energy power generation in different time periods. For example, by analyzing historical meteorological data and the operating data of new energy units, it is found that the power generation efficiency in certain areas is higher in a specific time period, thus providing a basis for the site selection and layout of new energy units. Through the data mining platform, meteorological data, energy management data, remote sensing data and geographic information data are collected, and through data mining on spatial and temporal scales, more comprehensive and accurate new energy data are obtained to understand the distribution and change patterns of new energy resources.

[0020] The spatial characteristic analysis module 12 is used to perform volatility and intermittency analysis on the new energy spatial data based on the spatial scale to obtain new energy spatial characteristics.

[0021] Specifically, the geographic space of new energy data is divided into a series of grid cells, and the power density of new energy in each grid cell is evaluated to understand the abundance and distribution of new energy resources in different regions. After obtaining the power density data, the availability of new energy in each grid cell is evaluated to determine whether the new energy can be effectively utilized in the grid cell. Based on the results of the availability evaluation, a volatility analysis is performed to understand the stability of new energy at different time points. Volatility refers to the spatial instability or variability of new energy production. At the spatial scale, volatility analysis involves comparing new energy production data in different geographical locations to identify the patterns and laws of production fluctuations. Based on the volatility analysis, an intermittent analysis is performed according to the preset intermittent time threshold to identify possible interruptions in new energy production. Intermittency refers to the interruption or decline in new energy production in space. At the spatial scale, intermittent analysis involves identifying which regions have interruptions or declines in new energy production and the reasons for these situations. The results of volatility and intermittency analysis are integrated to obtain the spatial characteristics of new energy. The volatility and intermittency analysis at the spatial scale helps to understand the spatial distribution characteristics of new energy production.

[0022] The time characteristic analysis module 13 is used to perform volatility and intermittency analysis on the new energy time data based on the time scale to obtain the new energy time characteristic.

[0023] Specifically, the time dimension of new energy data is divided into a series of time periods or time points, and each time grid unit represents a specific time period. On the basis of time grid division, the volatility and intermittency of new energy output in each time grid unit are analyzed to obtain the new energy volatility time distribution and the new energy intermittent time distribution. The larger the fluctuation amplitude, the worse the power generation stability of the new energy resources. The greater the intermittency, the worse the power generation reliability of the new energy resources. The analysis results of the volatility time distribution and the intermittent time distribution are integrated to obtain the time characteristics of new energy. The analysis of volatility and intermittency on the time scale helps to understand the changing characteristics of new energy output over time.

[0024] The complementary analysis module 14 is used to perform a complementary analysis of the spatial characteristics of the new energy and the temporal characteristics of the new energy to obtain complementary characteristics of the new energy.

[0025] Specifically, the turbulence of new energy sources in time and space is analyzed to identify the complementary characteristics between different new energy sources. Complementarity refers to the mutual compensation effect of different new energy sources on production fluctuations, that is, when the production of one new energy source decreases, the production of another new energy source may increase, thereby achieving stable energy production. For example, wind energy and solar energy are two common new energy sources, and there is complementarity in production fluctuations in time and space. In certain time periods or regions, wind energy production may be high, while solar energy production is low; while in other time periods or regions, the situation may be the opposite. Based on the results of complementarity analysis, the complementary characteristics of new energy sources are obtained, including the mutual compensation mode, degree of complementarity and range of complementarity of different new energy sources in production fluctuations. For example, in some regions, there is a high degree of complementarity between wind energy and solar energy, while in other regions, other types of energy need to be considered for complementarity. According to the results of complementarity analysis, the optimal combination of new energy resources is determined to balance the overall turbulence and improve the stability and reliability of the energy system. By identifying the complementary characteristics of new energy sources, production fluctuations can be coped with, energy production can be stabilized, and the stability of the new energy system can be improved.

[0026] The load planning module 15 is used to perform load planning of the new energy units based on the map tool platform and according to the complementary characteristics of the new energy.

[0027] Specifically, the complementary characteristics of renewable energy sources are integrated into the map tool platform. Analyze the load demand in different time periods and regions, including the analysis of historical load data and the study of future load forecasts. Analyze the load demand in different time periods and regions, including the analysis of historical load data and the study of future load forecasts. According to the complementary characteristics of renewable energy sources and load demand, formulate load planning strategies, including determining the operating status of different units in various time periods, and optimizing the startup, shutdown and scheduling of units. Load supply planning based on load demand and the complementary characteristics of renewable energy sources will help to achieve a better match between energy supply and demand and improve the stability and reliability of the energy system.

[0028] Furthermore, the spatial characteristic analysis module 12 in the spatial and temporal characteristic analysis system of new energy generating units based on a map tool is also used for:

[0029] The new energy spatial data is divided into spatial grids, and the new energy power density in the spatial grids is evaluated to obtain the new energy spatial grid distribution; the new energy availability is evaluated to obtain the availability new energy spatial grid distribution; the volatility analysis is performed based on the availability new energy spatial grid distribution to obtain the new energy space volatility characteristics; based on the preset intermittent time threshold, the availability new energy spatial grid distribution is analyzed to obtain the new energy space intermittent characteristics; the new energy space volatility characteristics and the new energy space intermittent characteristics are integrated to obtain the new energy space characteristics.

[0030] Specifically, the geographic space of new energy data is divided into a series of grid cells. Each grid cell represents a specific geographical area. Based on the spatial grid division, the power density of new energy in each grid cell is evaluated. Power density refers to the output of new energy per unit area or unit volume. Taking wind resources as an example, in each grid, the wind power density in the grid is evaluated by analyzing historical meteorological data and the operating data of wind turbines. Wind power density is an important indicator to measure the richness of wind energy resources, which indicates the average power of wind energy resources per unit area. The average wind power density in Northeast, Northwest and North China is usually greater than 200W / m 2 The average wind power density in eastern Inner Mongolia, the Qinghai-Tibet Plateau and northern Gansu is greater than 300W / m 2 The average wind power density in Hami, Xinjiang is even greater than 700W / m 2 , indicating that these areas have abundant wind energy resources and are suitable for developing wind power generation projects. However, the average wind power density in most areas of the south is less than 100W / m 2 , which means that these areas have relatively few wind energy resources and are not suitable for developing wind power generation projects. Through spatial grid division and new energy power density assessment, the new energy spatial grid distribution map is obtained, which helps to understand the spatial distribution of new energy resources.

[0031] After obtaining the spatial grid distribution of new energy, the availability of new energy in each grid unit is evaluated. The availability of new energy takes into account a variety of factors, such as weather conditions, equipment maintenance status, etc., to determine whether new energy can be effectively utilized in the grid unit. Taking wind resources as an example, the availability of wind resources in each grid unit is evaluated, that is, the proportion of time that wind energy can be utilized. The availability of wind energy resources refers to the proportion of wind energy resources effectively utilized by wind power generators within a certain period of time. The higher the proportion, the greater the utilization value of wind energy resources. The availability of wind energy resources in most areas is greater than 10%, which means that wind energy resources have a high utilization value in these areas. In onshore areas such as the western part of Northeast China, Inner Mongolia, northern Gansu, the Qinghai-Tibet Plateau and Hami, the availability of wind energy resources is greater than 28%, which has a high potential for wind energy resource development and is suitable for the construction of large-scale wind power generation projects. In most offshore areas, due to the lack of terrain obstacles, the availability of wind resources is higher than 50%, or even higher. The availability of new energy spatial grid distribution map obtained through the new energy availability assessment is helpful to understand the availability of new energy resources in practical applications.

[0032] Based on the results of the availability assessment, a volatility analysis is conducted. Volatility refers to the short-term changes in the output of new energy over time. By analyzing the volatility of new energy output, we can understand the stability of new energy at different time points. Taking wind resources as an example, the stability of wind resource power generation in each grid unit is analyzed, and the wind speed data of each grid unit is statistically analyzed. Indicators such as standard deviation and coefficient of variation are calculated to measure the amplitude and frequency of fluctuations. The amplitude of fluctuation refers to the degree of change in wind power output over time. The smaller the amplitude of fluctuation, the more stable the wind resources and the higher the stability of wind power generation. The frequency of fluctuation refers to the speed of change in wind power output. The higher the frequency, the faster the change in wind energy resources, which puts higher requirements on the design and operation of wind power generation systems.

[0033] On the basis of volatility analysis, intermittency analysis is performed according to the preset intermittent time threshold. Intermittency refers to the interruption or decline of new energy production within a certain period of time. It identifies possible interruptions in new energy production, thereby providing a basis for energy storage and scheduling. A pre-set intermittent time threshold is used to define the shortest length of time that new energy is continuously available or unavailable. Taking wind resources as an example, the length of time that wind resources in each grid unit remain continuously available or unavailable is analyzed as the intermittent law of wind resources. For example, the average length of time that wind resources in eastern Inner Mongolia and Hami region remain available continuously is between 15 and 20 hours, the average length of time that wind resources in western Northeast China, western Inner Mongolia, Qinghai-Tibet Plateau and northern Gansu remain available continuously is between 8 and 12 hours, and the average length of time that offshore wind resources remain available continuously is usually greater than 20 hours or even longer, indicating that wind energy resources in these areas have good sustainability and reliability and are suitable for the development of wind power generation projects.

[0034] Integrate the results of volatility and intermittency analysis to obtain the comprehensive spatial characteristics of new energy, including the spatial distribution, stability, availability and intermittency of new energy output, and provide a scientific basis for the optimal layout and operation of new energy. Through spatial grid division and power density assessment, accurately understand the spatial distribution and abundance of new energy, conduct availability, volatility and intermittency analysis, provide a scientific basis for the optimal layout and operation of new energy, and improve the efficiency and reliability of new energy systems.

[0035] Furthermore, the spatial characteristic analysis module 12 in the spatial and temporal characteristic analysis system of new energy generating units based on a map tool is also used for:

[0036] Performing a fluctuation amplitude analysis on the available new energy spatial grid distribution to obtain a new energy fluctuation amplitude grid distribution; presetting a fluctuation time threshold, performing a fluctuation rate calculation on the new energy fluctuation amplitude grid distribution to obtain a new energy fluctuation rate grid distribution, and adding the new energy fluctuation rate grid to the new energy spatial fluctuation characteristic.

[0037] Specifically, the fluctuation amplitude analysis of the spatial grid distribution of available new energy means calculating the fluctuation amplitude of new energy output in each grid unit. The fluctuation amplitude refers to the maximum range of fluctuation of new energy output in a certain period of time, which helps to understand the degree of fluctuation of new energy output in space, that is, which areas have larger fluctuations in new energy output and which areas have smaller fluctuations. In order to calculate the fluctuation rate, a time threshold is set in advance to determine how long to observe the fluctuation amplitude, such as 15 minutes, 1 hour, etc. The fluctuation rate is calculated for the grid distribution of new energy fluctuation amplitude to obtain the fluctuation rate of new energy output in each grid unit. The fluctuation rate refers to the speed at which new energy output fluctuates in unit time, which helps to understand the speed of new energy output fluctuation. The calculated grid distribution of new energy fluctuation rate is added to the spatial fluctuation characteristics of new energy. The spatial fluctuation characteristics of new energy include not only the fluctuation amplitude but also the fluctuation rate, thereby more comprehensively describing the fluctuation characteristics of new energy output. Through the analysis of fluctuation amplitude and fluctuation rate, we can have a deep understanding of the fluctuation characteristics of new energy output, which is helpful to predict and respond to changes in new energy output.

[0038] Furthermore, the spatial characteristic analysis module 12 in the spatial and temporal characteristic analysis system of new energy generating units based on a map tool is also used for:

[0039] Obtain the amplitude modulation correlation relationship between the new energy fluctuation amplitude and the new energy fluctuation amplitude modulation; perform fluctuation amplitude modulation on the new energy fluctuation amplitude grid distribution according to the amplitude modulation correlation relationship to obtain the new energy fluctuation amplitude modulation grid distribution, and add the new energy fluctuation amplitude modulation grid distribution to the new energy spatial fluctuation characteristic.

[0040] Specifically, the fluctuation amplitude of new energy refers to the maximum fluctuation range of new energy output within a certain period of time, and the fluctuation amplitude modulation of new energy refers to the rate of change of the fluctuation amplitude of new energy, that is, the change of the fluctuation amplitude over time. The amplitude modulation correlation relationship refers to the correlation between the fluctuation amplitude of new energy and the fluctuation amplitude modulation. Taking wind resources as an example, the large-scale fluctuation of wind speed has an amplitude modulation effect on the small-scale fluctuation, which means that in areas with large large-scale wind speed fluctuations, the amplitude of small-scale wind speed fluctuations will also be affected, and may increase or decrease. Based on the obtained amplitude modulation correlation relationship, the fluctuation amplitude modulation analysis of the grid distribution of the fluctuation amplitude of new energy is carried out, and the fluctuation amplitude modulation of the new energy output in each grid unit is calculated, that is, the rate of change of the fluctuation amplitude over time. The grid distribution of the amplitude-modulated fluctuation amplitude is integrated into the spatial fluctuation characteristics of new energy to provide a more comprehensive analysis. By obtaining the amplitude modulation correlation relationship and conducting the fluctuation amplitude modulation analysis, we can gain a deep understanding of the fluctuation characteristics of new energy output and provide more accurate data support for the optimized layout and operation of new energy.

[0041] Furthermore, the time characteristic analysis module 13 in the time-space characteristic analysis system of new energy units based on map tools is also used for:

[0042] Divide the new energy time data into time grids to obtain new energy time grid distribution; perform characteristic analysis on the new energy time grid distribution based on new energy volatility and new energy intermittency to obtain new energy volatility time distribution and new energy intermittent time distribution, and add the new energy volatility time distribution and the new energy intermittent time distribution to the new energy time characteristics.

[0043] Specifically, the time dimension of new energy data is divided into a series of time periods or time points. Each time grid unit represents a specific time period, such as hours, days, weeks or months, which helps to analyze the changes in new energy production over time and reveal the distribution characteristics in time. Taking wind resources as an example, time grid division can be based on different time scales. For example, a day is divided into 12 time periods, with hours as the unit, to analyze the daily changes in wind energy resources. Or the time grid is divided according to the month to analyze the monthly changes in wind energy resources, understand the changes in wind energy generation in different months, and the impact of seasonal factors on wind energy resources. The volatility of new energy production in each time grid unit is analyzed to understand the stability of new energy at different time points. Volatility refers to the short-term changes in new energy production over time. Also based on the time grid division, the intermittent nature of new energy production in each time grid unit is analyzed to identify possible interruptions in new energy production. Intermittency refers to the interruption or decline in new energy production within a certain period of time. The analysis results of the time distribution of volatility and the time distribution of intermittency are integrated to obtain the time characteristics of new energy, including the change pattern, stability, availability and intermittency of new energy production over time, providing a scientific basis for the optimal layout and operation of new energy. Understanding the volatility and intermittency of renewable energy production through time gridding and characteristic analysis can help predict and respond to changes in renewable energy production, thereby optimizing energy scheduling.

[0044] Furthermore, the complementary analysis module 14 in the map tool-based new energy unit spatiotemporal characteristic analysis system is also used for:

[0045] The new energy turbulence of the new energy spatial data is analyzed based on the new energy spatial characteristics, and the new energy turbulence of the new energy temporal data is analyzed based on the new energy temporal characteristics, and the new energy spatiotemporal turbulence is obtained by combining them; according to the new energy spatiotemporal turbulence, the new energy data is analyzed for complementarity to obtain the new energy complementary characteristics.

[0046] Specifically, the spatial characteristics of new energy, such as fluctuation amplitude, fluctuation rate and fluctuation modulation, are used to analyze the spatial data of new energy, so as to obtain the turbulence of new energy, which reflects the degree of fluctuation and speed of change of new energy output in space. For example, due to geographical location or climatic conditions, the wind or solar energy output in some areas may fluctuate greatly, thus having a higher turbulence. The temporal characteristics of new energy, such as volatility and intermittency, are used to analyze the temporal data of new energy, so as to obtain the turbulence of new energy, which reflects the degree of change and stability of new energy output over time. For example, due to weather conditions or seasonal changes, the wind or solar energy output in some time periods may fluctuate greatly, thus having a higher turbulence. The analysis results of new energy turbulence based on spatial characteristics and temporal characteristics are combined to obtain the spatiotemporal turbulence of new energy. Spatiotemporal turbulence is a comprehensive indicator that takes into account the volatility and instability of new energy output in space and time.

[0047] Taking wind resources as an example, the influence of land and sea topography on the turbulence of new energy is analyzed. Due to the diversity of land topography, the roughness of the surface is large, and the irregularity of the surface will lead to strong dynamic turbulence. In contrast, due to the smoothness of seawater, the surface roughness of the sea area is small, so the drag effect on the wind is small, and the turbulence of wind resources is relatively small. At the same time, the diversity of land topography and surface and the local weather processes caused by it make the changes of land wind resources over time more complex and diverse. The undulating terrain, obstacle distribution and surface cover type on land will affect the flow and turbulence characteristics of wind, making the volatility and intermittency of land wind resources more significant. On the other hand, the specific heat capacity of seawater is usually greater than that of air, which means that the temperature change of sea level water vapor in a day is smaller than the temperature change of the air above it. Due to the high heat capacity of seawater, it absorbs more heat and changes less in temperature. In contrast, the heat capacity of air is low, and it is easy to absorb and release heat, resulting in large temperature changes. This characteristic makes the wind resources in the offshore area relatively stable in temperature changes, thus affecting the turbulence of offshore wind resources. By analyzing the spatiotemporal turbulence of new energy, we can gain an in-depth and comprehensive understanding of the changing characteristics of new energy output, which will help predict and respond to changes in new energy output, thereby optimizing energy scheduling and improving the stability and reliability of the power grid.

[0048] Furthermore, the load planning module 15 in the map tool-based new energy unit spatiotemporal characteristic analysis system is also used for:

[0049] The new energy complementary characteristics are configured on the map tool platform; and the load supply planning of the new energy unit based on the map tool platform is executed according to the load demand to obtain the load strategy.

[0050] Specifically, the complementary characteristics of new energy sources are integrated into the map tool platform, and the complementary characteristics of different new energy units are displayed on the map, such as the complementary relationship between wind energy and solar energy, as well as the change patterns in space and time. Based on the complementary characteristics of new energy sources on the map tool platform, the load supply planning of new energy units is carried out according to the actual load demand, including determining which new energy units should be put into operation in different time periods and the operating ratio. Load supply planning needs to consider the performance, complementary characteristics, load demand and possible external factors of new energy units, such as weather conditions or market demand. Through load supply planning, the load strategy is obtained, that is, how to reasonably configure and dispatch new energy units in different time periods and geographical locations to meet the load demand and improve the utilization efficiency of new energy resources. By configuring the complementary characteristics of new energy sources on the map tool platform and conducting energy planning and scheduling, it is helpful to achieve a better match between energy supply and demand and improve the stability and reliability of the energy system.

[0051] In summary, the spatiotemporal characteristics analysis system of new energy units based on map tools provided by this application has the following technical effects:

[0052] The data mining module is used to integrate the meteorological platform, energy management platform, remote sensing data platform and geographic information system to establish a data mining platform, and the data mining platform is used to perform new energy data mining based on spatial scale and time scale to obtain new energy spatial data and new energy time data; the spatial characteristic analysis module is used to perform volatility and intermittency analysis on the new energy spatial data based on the spatial scale to obtain new energy spatial characteristics; the time characteristic analysis module is used to perform volatility and intermittency analysis on the new energy time data based on the time scale to obtain new energy time characteristics; the complementary analysis module is used to perform complementary analysis on the new energy spatial characteristics and the new energy time characteristics to obtain new energy complementary characteristics; the load planning module is used to perform load planning of new energy units based on the map tool platform according to the new energy complementary characteristics. In other words, by integrating multi-source data and mining data based on spatial scale and time scale, the layout and operation strategy of new energy units are optimized to improve the utilization efficiency of new energy.

[0053] Embodiment 2, based on the same inventive concept as the system for analyzing the spatiotemporal characteristics of a new energy generator set based on a map tool in the previous embodiment, the present application also provides a method for analyzing the spatiotemporal characteristics of a new energy generator set based on a map tool, please refer to the attached Figure 2 , the method for analyzing the spatiotemporal characteristics of new energy units based on a map tool includes:

[0054] A data mining platform is established by integrating a meteorological platform, an energy management platform, a remote sensing data platform and a geographic information system. New energy data mining based on spatial scale and temporal scale is performed through the data mining platform to obtain new energy spatial data and new energy temporal data; volatility and intermittency analysis is performed on the new energy spatial data based on the spatial scale to obtain new energy spatial characteristics; volatility and intermittency analysis is performed on the new energy temporal data based on the temporal scale to obtain new energy temporal characteristics; complementarity analysis is performed on the new energy spatial characteristics and the new energy temporal characteristics to obtain new energy complementary characteristics; based on a map tool platform, load planning of new energy units is performed according to the new energy complementary characteristics.

[0055] Further, the volatility and intermittency analysis of the new energy spatial data based on the spatial scale to obtain new energy spatial characteristics includes:

[0056] The new energy spatial data is divided into spatial grids, and the new energy power density in the spatial grids is evaluated to obtain the new energy spatial grid distribution; the new energy availability is evaluated to obtain the availability new energy spatial grid distribution; the volatility analysis is performed based on the availability new energy spatial grid distribution to obtain the new energy space volatility characteristics; based on the preset intermittent time threshold, the availability new energy spatial grid distribution is analyzed to obtain the new energy space intermittent characteristics; the new energy space volatility characteristics and the new energy space intermittent characteristics are integrated to obtain the new energy space characteristics.

[0057] Further, the volatility analysis is performed according to the spatial grid distribution of the available new energy to obtain the spatial volatility characteristics of the new energy, including:

[0058] Performing a fluctuation amplitude analysis on the available new energy spatial grid distribution to obtain a new energy fluctuation amplitude grid distribution; presetting a fluctuation time threshold, performing a fluctuation rate calculation on the new energy fluctuation amplitude grid distribution to obtain a new energy fluctuation rate grid distribution, and adding the new energy fluctuation rate grid to the new energy spatial fluctuation characteristic.

[0059] Further, the volatility analysis is performed according to the spatial grid distribution of the available new energy to obtain the spatial volatility characteristics of the new energy, including:

[0060] Obtain the amplitude modulation correlation relationship between the new energy fluctuation amplitude and the new energy fluctuation amplitude modulation; perform fluctuation amplitude modulation on the new energy fluctuation amplitude grid distribution according to the amplitude modulation correlation relationship to obtain the new energy fluctuation amplitude modulation grid distribution, and add the new energy fluctuation amplitude modulation grid distribution to the new energy spatial fluctuation characteristic.

[0061] Further, the volatility and intermittency analysis of the new energy time data based on the time scale to obtain the new energy time characteristics includes:

[0062] Divide the new energy time data into time grids to obtain new energy time grid distribution; perform characteristic analysis on the new energy time grid distribution based on new energy volatility and new energy intermittency to obtain new energy volatility time distribution and new energy intermittent time distribution, and add the new energy volatility time distribution and the new energy intermittent time distribution to the new energy time characteristics.

[0063] Further, the complementary analysis of the new energy space characteristics and the new energy time characteristics is performed to obtain the complementary characteristics of the new energy, including:

[0064] The new energy turbulence of the new energy spatial data is analyzed based on the new energy spatial characteristics, and the new energy turbulence of the new energy temporal data is analyzed based on the new energy temporal characteristics, and the new energy spatiotemporal turbulence is obtained by combining them; according to the new energy spatiotemporal turbulence, the new energy data is analyzed for complementarity to obtain the new energy complementary characteristics.

[0065] Furthermore, the map tool platform is used to carry out load planning of new energy units according to the complementary characteristics of new energy sources, including:

[0066] The new energy complementary characteristics are configured on the map tool platform; and the load supply planning of the new energy unit based on the map tool platform is executed according to the load demand to obtain the load strategy.

[0067] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. Figure 1 The spatiotemporal characteristics analysis system of a new energy unit based on a map tool and the specific examples in the first embodiment are also applicable to the spatiotemporal characteristics analysis method of a new energy unit based on a map tool in this embodiment. Through the above detailed description of the spatiotemporal characteristics analysis system of a new energy unit based on a map tool, those skilled in the art can clearly know the spatiotemporal characteristics analysis method of a new energy unit based on a map tool in this embodiment, so for the sake of brevity of the specification, it will not be described in detail here. For the method disclosed in the embodiment, since it corresponds to the system disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the system description.

[0068] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0069] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the present application and its equivalent technology, the present application is also intended to include these modifications and variations.

Claims

1. A new energy unit spatiotemporal characteristics analysis system based on map tools, characterized in that: include: A data mining module is used to integrate a meteorological platform, an energy management platform, a remote sensing data platform and a geographic information system to establish a data mining platform, and to perform new energy data mining based on spatial scale and temporal scale through the data mining platform to obtain new energy spatial data and new energy temporal data; A spatial characteristic analysis module, used for performing volatility and intermittency analysis on the new energy spatial data based on the spatial scale to obtain new energy spatial characteristics; A time characteristic analysis module, used for performing volatility and intermittency analysis on the new energy time data based on the time scale to obtain the new energy time characteristic; A complementary analysis module, used to perform a complementary analysis of the spatial characteristics of the new energy and the temporal characteristics of the new energy to obtain complementary characteristics of the new energy; The load planning module is used to carry out load planning of new energy units based on a map tool platform and according to the complementary characteristics of the new energy.

2. A new energy unit spatiotemporal characteristics analysis system based on a map tool as claimed in claim 1, characterized in that: The spatial characteristic analysis module is also used for: Dividing the new energy spatial data into spatial grids, and evaluating the new energy power density within the spatial grids to obtain the new energy spatial grid distribution; Performing a new energy availability assessment on the new energy spatial grid distribution to obtain an available new energy spatial grid distribution; Performing a volatility analysis based on the spatial grid distribution of the available new energy to obtain the spatial volatility characteristics of the new energy; Based on the preset intermittent time threshold, the intermittent analysis of the spatial grid distribution of the available new energy is carried out to obtain the spatial intermittent characteristics of the new energy; The new energy space fluctuation characteristics and the new energy space intermittent characteristics are integrated to obtain the new energy space characteristics.

3. A new energy unit spatiotemporal characteristics analysis system based on a map tool as claimed in claim 2, characterized in that: The spatial characteristic analysis module is also used for: Performing a fluctuation amplitude analysis on the spatial grid distribution of the available new energy to obtain a grid distribution of the new energy fluctuation amplitude; A fluctuation time threshold is preset, and a fluctuation rate is calculated for the grid distribution of the new energy fluctuation amplitude to obtain a new energy fluctuation rate grid distribution, and the new energy fluctuation rate grid is added to the new energy spatial fluctuation characteristic.

4. A new energy unit spatiotemporal characteristics analysis system based on a map tool as claimed in claim 3, characterized in that: The spatial characteristic analysis module is also used for: Obtaining the correlation between the amplitude of new energy fluctuation and the amplitude modulation of new energy fluctuation; The new energy fluctuation amplitude grid distribution is subjected to fluctuation amplitude modulation according to the amplitude modulation correlation relationship to obtain the new energy fluctuation amplitude modulation grid distribution, and the new energy fluctuation amplitude modulation grid distribution is added to the new energy spatial fluctuation characteristic.

5. The time-space characteristic analysis system of new energy units based on map tools as claimed in claim 1, characterized in that: The time characteristic analysis module is also used for: Dividing the new energy time data into time grids to obtain new energy time grid distribution; Based on the volatility and intermittency of new energy, the characteristics of the new energy time grid distribution are analyzed to obtain the new energy volatility time distribution and the new energy intermittent time distribution, and the new energy volatility time distribution and the new energy intermittent time distribution are added to the new energy time characteristics.

6. A new energy unit spatiotemporal characteristics analysis system based on a map tool as claimed in claim 1, characterized in that: The complementary analysis module is also used for: Analyze the new energy turbulence of the new energy spatial data based on the new energy spatial characteristics, analyze the new energy turbulence of the new energy temporal data based on the new energy temporal characteristics, and combine to obtain the new energy spatiotemporal turbulence; According to the spatiotemporal turbulence of the new energy, a complementarity analysis is performed on the new energy data to obtain the complementary characteristics of the new energy.

7. A new energy unit spatiotemporal characteristics analysis system based on a map tool as claimed in claim 1, characterized in that: The load planning module is also used for: Configuring the new energy complementary feature on the map tool platform; The load supply planning of the new energy unit based on the map tool platform is executed according to the load demand to obtain the load strategy.

8. A method for analyzing the spatiotemporal characteristics of new energy units based on map tools, characterized in that: The method is performed by a new energy unit spatiotemporal characteristic analysis system based on a map tool according to any one of claims 1 to 7, wherein the new energy unit spatiotemporal characteristic analysis method based on a map tool comprises: Integrate the meteorological platform, energy management platform, remote sensing data platform and geographic information system to establish a data mining platform, and perform new energy data mining based on spatial scale and temporal scale through the data mining platform to obtain new energy spatial data and new energy temporal data; Performing volatility and intermittency analysis on the new energy spatial data based on the spatial scale to obtain new energy spatial characteristics; Performing volatility and intermittency analysis on the new energy time data based on the time scale to obtain new energy time characteristics; Performing a complementarity analysis between the spatial characteristics of the new energy and the temporal characteristics of the new energy to obtain complementary characteristics of the new energy; Based on the map tool platform, load planning of new energy units is carried out according to the complementary characteristics of new energy.

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