Optimal configuration method, device and system for wind and light storage mobile power supply in complex environment

Abstract

The invention relates to the technical field of power systems, in particular to a wind and light storage mobile power supply optimization configuration method, device and system in a complex environment. The method comprises the steps of dividing a configuration place into a plurality of power supply points according to a power supply area, obtaining environment data of each power supply point, and predicting wind power and photovoltaic power generation power of each power supply point according to the environment data of the power supply points; acquiring real-time demand data of a power supply point, and predicting a power demand load change trend; acquiring wind power and photovoltaic power generation power data of a power supply point and a power demand load change trend, selecting wind and light storage equipment, and performing configuration optimization; the device comprises a power supply point power prediction module, a power supply point power demand prediction module and an equipment configuration optimization module. The wind power and photovoltaic power generation power of a power supply point in a complex environment and the power demand load change trend are respectively predicted, and the corresponding wind and light storage equipment is selected and configured and optimized, so that the energy utilization efficiency and the power supply guarantee capability are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and particularly to an optimization configuration method, device and system for a wind-solar-storage mobile power supply in a complex environment. Background Art

[0002] With the continuous growth of global energy demand and the increasing emphasis on environmental protection and sustainable development, the application of renewable energy in power systems is becoming more and more extensive. As two of the most promising renewable energy sources, wind energy and solar energy have the advantages of rich resources, wide distribution, clean and pollution-free, etc., and have become an important way to solve the energy crisis and environmental problems.

[0003] Currently, in the planning of renewable energy power supply systems, the differences in environmental data of different power supply points are not fully considered, and the prediction of wind power and photovoltaic power is not accurate enough, which affects the reasonable configuration of wind-solar-storage equipment, thereby reducing the energy utilization efficiency and power supply guarantee ability in complex environments. Summary of the Invention

[0004] The purpose of the present invention is to provide an optimization configuration method, device and system for a wind-solar-storage mobile power supply in a complex environment, aiming to solve the technical problem in the prior art that the prediction of wind power and photovoltaic power is not accurate enough, which affects the reasonable configuration of wind-solar-storage equipment, thereby reducing the energy utilization efficiency and power supply guarantee ability in complex environments.

[0005] To achieve the above purpose, a method for optimizing the configuration of a wind-solar-storage mobile power supply in a complex environment adopted by the present invention includes the following steps: Divide the power supply area into multiple power supply points according to the geographical features, and obtain the environmental data of each power supply point. For the environmental data of the power supply points, predict the wind power and photovoltaic power of each power supply point; Obtain the real-time demand data of the power supply points, and predict the change trend of the power demand load; Obtain the wind power, photovoltaic power data and the change trend of the power demand load of the power supply points, select the wind-solar-storage equipment, and perform configuration optimization.

[0006] Among them, in the step of dividing the power supply area into multiple power supply points according to the geographical features, obtaining the environmental data of each power supply point, and predicting the wind power and photovoltaic power of each power supply point for the environmental data of the power supply points: Divide the power supply area into multiple sub-areas according to the geographical features, set multiple power supply points in each sub-area, and set sensors to obtain the environmental data of the power supply points; among them, the environmental data includes wind, light, and temperature data; Perform data processing on environmental data and extract key features; among them, the data processing methods include data cleaning, filtering, normalization, and outlier removal, and the key features include wind speed volatility and light attenuation trend; Obtain the wind speed volatility feature and predict the wind power of the power supply point; Obtain the light attenuation trend feature and predict the photovoltaic power generation of the power supply point.

[0007] Among them, in the step of obtaining the wind speed volatility feature and predicting the wind power of the power supply point: Collect historical wind speed data and corresponding wind power data to train the wind power prediction model, obtain the wind speed volatility feature, perform the prediction of wind power, and output the prediction result.

[0008] Among them, in the step of obtaining the light attenuation trend feature and predicting the photovoltaic power generation of the power supply point: Collect historical light data and corresponding light attenuation trend data to train the photovoltaic power generation prediction model, obtain the light attenuation trend feature, perform the prediction of photovoltaic power generation, and output the prediction result.

[0009] Among them, in the step of obtaining the real-time demand data of the power supply point and predicting the change trend of the power demand load: Collect the load data of the power supply point; among them, the load data includes load power, current, and voltage data; Obtain the historical power consumption data of the power supply point and identify the load usage of the power supply point; Respectively obtain the load data and load usage of the power supply point, and predict the change trend of the power demand load.

[0010] Among them, in the step of respectively obtaining the load data and load usage of the power supply point and predicting the change trend of the power demand load: Extract load power, current, voltage, and the average value, maximum value, minimum value, and change rate of the load power within the historical time from the load data and load usage as features, construct a load change trend prediction model, and input the real-time collected load data and the extracted features into the load change trend prediction model to predict the change trend of the demand load.

[0011] Among them, in the step of obtaining the wind power and photovoltaic power generation data of the power supply point and the change trend of the power demand load, selecting the wind-solar-storage equipment, and performing configuration optimization: According to the wind power prediction result, select the model of the power generation equipment with a rated power match; According to the photovoltaic power prediction and installation area, select the type of photovoltaic panel; among them, the types of photovoltaic panels include monocrystalline silicon, polycrystalline silicon, and thin-film photovoltaic panels; According to the load fluctuation range and power generation intermittency, select the energy storage equipment.

[0012] Among them, in the steps of obtaining the wind power generation power and photovoltaic power generation power data of the power supply point and the change trend of the power demand load, selecting the wind-solar-storage equipment, and performing configuration optimization: Monitor the operating status of the power generation equipment, photovoltaic panels, and energy storage equipment respectively, compare the deviation between the actual power generation power, load demand and the predicted value, and dynamically adjust the configuration strategy according to the deviation data.

[0013] The present invention also provides an optimized configuration device for a wind-solar-storage mobile power supply in a complex environment, including a power supply point power prediction module, a power supply point power demand prediction module, and an equipment configuration optimization module; wherein: The power supply point power prediction module is used to divide the power supply area into multiple power supply points according to the configuration, obtain the environmental data of each power supply point, and predict the wind power generation power and photovoltaic power generation power of each power supply point according to the environmental data of the power supply point; The power supply point power demand prediction module is used to obtain the real-time demand data of the power supply point and predict the change trend of the power demand load; The equipment configuration optimization module is used to obtain the wind power generation power and photovoltaic power generation power data of the power supply point and the change trend of the power demand load, select the wind-solar-storage equipment, and perform configuration optimization.

[0014] The present invention also provides an optimized configuration system for a wind-solar-storage mobile power supply in a complex environment, including a processor, a network interface, and a memory. The processor, the network interface, and the memory are connected to each other. Among them, the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions to execute the optimized configuration method for the wind-solar-storage mobile power supply in the complex environment.

[0015] An optimized configuration method, device, and system for a wind-solar-storage mobile power supply in a complex environment of the present invention respectively adopt the power supply point power prediction module, the power supply point power demand prediction module, and the equipment configuration optimization module to perform the following steps: divide the power supply area into multiple power supply points according to the configuration, and obtain the environmental data of each power supply point. According to the environmental data of the power supply point, predict the wind power generation power and photovoltaic power generation power of each power supply point; obtain the real-time demand data of the power supply point and predict the change trend of the power demand load; obtain the wind power generation power and photovoltaic power generation power data of the power supply point and the change trend of the power demand load, select the wind-solar-storage equipment, and perform configuration optimization; by respectively predicting the wind power generation power and photovoltaic power generation power of the power supply point in the complex environment and the change trend of the power demand load, select the corresponding wind-solar-storage equipment and perform configuration optimization to improve the energy utilization efficiency and power supply guarantee ability. Description of the Drawings

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0017] Figure 1 It is a schematic flowchart of the optimization configuration method of the wind-solar-storage mobile power supply in a complex environment of the present invention.

[0018] Figure 2 It is a step flowchart of the optimization configuration method of the wind-solar-storage mobile power supply in a complex environment of the present invention.

[0019] Figure 3 It is a step flowchart of S100 of the present invention.

[0020] Figure 4 It is a step flowchart of S200 of the present invention.

[0021] Figure 5 It is a step flowchart of S300 of the present invention.

[0022] Figure 6 It is a structural schematic diagram of the optimization configuration device of the wind-solar-storage mobile power supply in a complex environment of the present invention.

[0023] Figure 7 It is a structural schematic diagram of the optimization configuration system of the wind-solar-storage mobile power supply in a complex environment of the present invention.

[0024] 401 - Power prediction module of the power supply point, 402 - Power demand prediction module of the power supply point, 403 - Equipment configuration optimization module. Specific embodiments

[0025] Here, the exemplary embodiments will be described in detail, and the examples are shown in the drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application.

[0026] The terms used in the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. The singular forms of "a", "the" and "said" used in the present application and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0027] It should be understood that although the terms first, second, third, etc. may be used in this application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from each other. For example, without departing from the scope of this application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "when" or "while" or "in response to determining".

[0028] Please refer to Figures 1 to 5 , the present invention provides an optimized configuration method for a wind-solar-storage mobile power supply in a complex environment, including the following steps: S100: Divide the power supply area into multiple power supply points according to the configuration area, and obtain the environmental data of each power supply point. For the environmental data of the power supply points, predict the wind power and photovoltaic power of each power supply point.

[0029] In this embodiment, the power supply area is divided into multiple power supply points according to the configuration area, and the environmental data of each power supply point is obtained. For the environmental data of the power supply points, predict the wind power and photovoltaic power of each power supply point. The specific process is as follows: S101: According to the geographical features, divide the power supply area into multiple sub-areas, set multiple power supply points in each sub-area, and set sensors to obtain the environmental data of the power supply points; wherein, the environmental data includes wind, light, and temperature data; S102: Perform data processing on the environmental data and extract key features; wherein, the data processing methods include data cleaning, filtering, normalization, and outlier removal, and the key features include wind speed volatility and light attenuation trend; S103: Obtain the wind speed volatility feature and predict the wind power of the power supply point; S104: Obtain the light attenuation trend feature and predict the photovoltaic power of the power supply point.

[0030] In the above process, according to geographical features, such as terrain, climate zone, etc., the power supply area is divided into multiple sub-areas. In each sub-area, according to factors such as power demand and power grid layout, multiple power supply points are reasonably set. Wind speed sensors, light sensors, and temperature sensors are installed at each power supply point to obtain wind, light, and temperature data in real time.

[0031] Perform data processing on the environmental data, including data cleaning to remove incorrect or invalid data; filtering to smooth data fluctuations; normalization to scale the data to a unified range; outlier removal to identify and remove abnormal data points; after the data processing is completed, extract the key features of wind speed volatility and light attenuation trend respectively.

[0032] Among them, in the extraction of the key features of wind speed volatility, the wind speed volatility F is calculated using the following formula v : where V i is the wind speed measurement value, V avg is the average wind speed, and N is the number of measurement points.

[0033] Among them, in the extraction of the key features of light attenuation trend, it is evaluated by analyzing the change of light intensity over time. For example, the change rate or attenuation ratio of light intensity in a day is calculated.

[0034] Obtain the wind speed volatility feature and predict the wind power at the power supply point; collect historical wind speed data and corresponding wind power data to train the wind power prediction model. According to the prediction requirements and data characteristics, a suitable prediction model can be selected. Common wind power prediction models include physical models, statistical models (such as time series analysis, regression analysis), and machine learning models (such as neural networks, support vector machines, random forests, etc.). Use historical wind speed data and corresponding wind power data to train the selected model. During the training process, optimize the prediction performance of the model by adjusting the model parameters. In addition to the wind speed volatility feature, other features related to wind power prediction (such as average wind speed, maximum wind speed, wind direction, temperature, etc.) can also be considered to be incorporated into the model to improve the prediction accuracy. Use the extracted and processed wind speed volatility feature and other relevant features as the input of the model to predict the wind power and output the prediction result.

[0035] Obtain the light attenuation trend characteristics and predict the photovoltaic power generation at the power supply point; collect historical light data and corresponding light attenuation trend data to train the photovoltaic power generation prediction model, obtain the light attenuation trend characteristics, and perform the prediction of photovoltaic power generation, and output the prediction results. Among them, a day is divided into multiple time periods (such as morning, noon, afternoon, evening), and the change of light intensity in each time period is analyzed separately. For each time period, calculate the attenuation rate of the light intensity. The attenuation rate can be obtained by comparing the light intensity at the beginning and end of the time period, or by analyzing the linear decline trend of the light intensity within the time period. For example, the average hourly decline rate of the light intensity can be calculated, or a more complex trend fitting method (such as an exponential decay model) can be used to estimate the attenuation trend. Quantify the calculated attenuation rate or other characteristics that can reflect the light attenuation trend (such as the change slope of the light intensity within a specific time period) as the input for the subsequent prediction model. In addition to the light attenuation trend characteristics, other data related to photovoltaic power generation, such as environmental data such as temperature, humidity, wind speed, and device parameters such as the tilt angle and orientation of the photovoltaic panels, can also be considered for fusion. According to the characteristics of the data and the prediction requirements, select a suitable prediction model. Common models include statistical models (such as multiple linear regression, time series analysis), machine learning models (such as random forest, support vector machine), or deep learning models (such as LSTM, GRU, etc., which are especially suitable for processing time series data). Use historical light data, light attenuation trend characteristics, and other relevant data as input, and the corresponding photovoltaic power generation as output to train the selected model. During the training process, optimize the model parameters through methods such as cross-validation and grid search to improve the prediction accuracy. Use the trained model to predict the new light attenuation trend characteristics to obtain the predicted value of photovoltaic power generation. Compare the predicted value with the actual value, and use evaluation metrics (such as mean squared error MSE, root mean squared error RMSE, mean absolute percentage error MAPE, etc.) to evaluate the prediction performance of the model.

[0036] S200: Obtain the real-time demand data of the power supply point and predict the change trend of the power demand load.

[0037] In this embodiment, obtain the real-time demand data of the power supply point and predict the change trend of the power demand load. The specific process is as follows: S201: Collect the load data of the power supply point; where the load data includes load power, current, and voltage data; S202: Obtain the historical power consumption data of the power supply point and identify the load usage of the power supply point; S203: Obtain the load data and load usage of the power supply point respectively, and predict the change trend of the power demand load.

[0038] During the above process, appropriate sensors are installed at key positions of the power supply points (such as distribution transformers, user access points, etc.) to measure the load power, current, and voltage data in real time. The measured data is transmitted to the data acquisition system by wired or wireless means. The data acquisition system is responsible for receiving, storing, and managing these data, and performing preliminary data verification and processing, such as removing noise and correcting error data. The collected load data is stored in a database for subsequent analysis and processing.

[0039] Extract the historical electricity consumption data of the power supply point from the database, including the time series data of load power, current, and voltage, as well as relevant information such as electricity consumption time and electricity consumption type (such as industrial electricity, commercial electricity, residential electricity, etc.). Identify the load usage of the power supply point through data analysis methods (such as statistical analysis, clustering analysis, etc.). For example, the load change rules in different time periods (such as weekdays, weekends, holidays) can be analyzed to identify the peak and off-peak electricity consumption periods; the load can also be classified according to the electricity consumption type to understand the electricity consumption characteristics and demands of different types of users.

[0040] For example: Identification of peak and off-peak electricity consumption periods: By analyzing the historical load power data, it is found that the peak electricity consumption periods of this power supply point are from 8:00 to 12:00 and from 18:00 to 22:00 on weekdays, with relatively high average load power; while the off-peak electricity consumption period is from 2:00 to 6:00 in the early morning, with relatively low average load power.

[0041] Classification of electricity consumption types: According to the electricity consumption nature and patterns of users, the users of the power supply point are divided into three categories: industrial users, commercial users, and residential users. The electricity consumption load of industrial users is relatively stable, but the electricity consumption is large; the electricity consumption load of commercial users has obvious differences between weekdays and weekends; the electricity consumption load of residential users is relatively high at night and on weekends.

[0042] Extract load power, current, voltage, as well as the average value, maximum value, minimum value, and change rate of load power within the historical time from the load data and load usage as features. The historical time can be a period of time before, such as within the previous 2 hours. Select appropriate prediction models, such as time series models (such as ARIMA model, exponential smoothing model), machine learning models (such as random forest model, support vector machine model), or deep learning models (such as LSTM model). Use the historical data to train the model and adjust the model parameters to improve the prediction accuracy of the model. Input the real-time collected load data and the extracted features into the trained prediction model to predict the change trend of the power demand load in the future period. The prediction results can include the predicted values of load power, electricity consumption, etc.

[0043] S300: Obtain the wind power and photovoltaic power data of the power supply point and the changing trend of the power demand load, select the wind-solar-storage equipment, and perform configuration optimization.

[0044] In this embodiment, obtain the wind power and photovoltaic power data of the power supply point and the changing trend of the power demand load, select the wind-solar-storage equipment, and perform configuration optimization. The specific process is as follows: S301: Select the model of the power generation equipment with a rated power matching according to the wind power prediction result; S302: Select the type of photovoltaic panel according to the photovoltaic power prediction and the installation area; the types of photovoltaic panels include monocrystalline silicon, polycrystalline silicon, and thin-film photovoltaic panels; S303: Select the energy storage equipment according to the load fluctuation range and the intermittency of power generation; S304: Monitor the operating states of the power generation equipment, photovoltaic panels, and energy storage equipment respectively, compare the deviations between the actual power generation power, load demand and the predicted values, and dynamically adjust the configuration strategy according to the deviation data.

[0045] In the above process, select the model of the wind power generation equipment with a rated power matching according to the predicted wind power data and the changing trend of the power demand load. For example, if the predicted average wind power is 500 kW and the maximum power is 800 kW, then a wind turbine generator set with a rated power of 600 kW to 800 kW can be selected to ensure that the equipment can operate efficiently for most of the time, and at the same time avoid the equipment being in an overloaded or underloaded state for a long time. Such as: If the average wind power demand at the power supply point is P avg , and the rated power of a single wind turbine generator set is P rated , considering the availability and redundancy of the equipment, the number N wind of the required wind turbine generator sets can be estimated by the following formula: Among them, wind is the availability of the wind turbine generator set, and the value is usually 0.8 to 0.95.

[0046] Select the type of photovoltaic panel according to the photovoltaic power prediction, installation area and the changing trend of the power demand load; compare different types of photovoltaic panels such as monocrystalline silicon, polycrystalline silicon, and thin-film photovoltaic panels, and consider factors such as conversion efficiency, cost, service life, temperature coefficient, and low-light performance. Among them: Monocrystalline silicon photovoltaic panel: The conversion efficiency is relatively high, generally between 18% and 22%, but the cost is relatively high; Polycrystalline silicon photovoltaic panel: The conversion efficiency is slightly lower than that of monocrystalline silicon, usually between 15% and 18%, and the cost is relatively low; Thin-film photovoltaic panels: They have a relatively low conversion efficiency, generally ranging from 10% to 15%, but they have advantages such as good flexibility, good low-light performance, and low cost; According to the power generation power demand per unit area, cost budget, and other actual requirements, select the appropriate type of photovoltaic panel. For example, if the installation area is limited and high power generation efficiency is required, monocrystalline silicon photovoltaic panels can be selected; if cost is the main consideration factor and the installation area is large, polycrystalline silicon photovoltaic panels can be selected; if it needs to be installed on a curved or irregular surface, or has high requirements for low-light performance, thin-film photovoltaic panels can be selected.

[0047] Among them, when calculating the required area of photovoltaic panels, the following calculation method can be adopted: If the average photovoltaic power generation power demand at the power supply point is P pv,avg , the conversion efficiency of the selected photovoltaic panel is pv , the average light intensity at the local area is I avg (unit: W / m 2 ), then the required area A pv of the photovoltaic panel can be calculated by the following formula: According to the load fluctuation range and the intermittency of power generation, analyze the load fluctuation range of the power demand load and the intermittency characteristics of wind power and photovoltaic power generation. For example, the load may suddenly increase or decrease during certain time periods, and wind power and photovoltaic power generation are affected by weather and natural conditions, having obvious intermittency and uncertainty.

[0048] According to the characteristics of the load and power generation, select the appropriate type of energy storage device, such as lead-acid batteries, lithium-ion batteries, flow batteries, supercapacitors, etc. Among them: Lead-acid batteries: They have a relatively low cost and mature technology, but have a relatively low energy density and a relatively short cycle life; Lithium-ion batteries: They have a high energy density, a long cycle life, and a high charge and discharge efficiency, but the cost is relatively high; Flow batteries: They have advantages such as large capacity, long life, and deep charge and discharge capabilities, but the cost is high and the system is complex; Supercapacitors: They have a fast charge and discharge speed and a high power density, but a relatively low energy density, and are suitable for power compensation within a short period of time; Determination of energy storage capacity and power: According to the load fluctuation range and the intermittency of power generation, determine the capacity and power of the energy storage device. The energy storage capacity should be able to meet the energy demand during power generation shortage or load peak, and the energy storage power should be able to quickly respond to the change of the load.

[0049] Among them, when estimating the capacity of the energy storage device: If during the power generation shortage or load peak period, the energy that the energy storage device needs to provide is Estorage (Unit: kWh). The charge-discharge efficiency of the energy storage device is storage , then the rated capacity C of the energy storage device storage (Unit: kWh) can be calculated by the following formula: Establish a perfect monitoring system to monitor the operating status of wind power generation equipment, photovoltaic panels and energy storage devices in real time, including parameters such as power generation power, voltage, current, and temperature. Regularly compare the deviation between the actual power generation power, load demand and the predicted value, and analyze the reasons for the deviation. For example, the deviation may be caused by factors such as weather changes, equipment failures, and inaccurate prediction models.

[0050] According to the deviation analysis results, dynamically adjust the configuration strategy of the wind-solar-storage equipment. For example, if it is found that the deviation between the predicted value and the actual value of the wind power is large, the operating parameters of the wind turbine generator can be adjusted or additional standby units can be added; if the photovoltaic power generation is insufficient, the installation area of the photovoltaic panels can be considered to be increased or the orientation and angle of the photovoltaic panels can be optimized; if the energy storage device cannot meet the load demand, the capacity of the energy storage device can be increased or the charge-discharge strategy of the energy storage device can be adjusted. For example: If in the actual operation of a certain day, it is found that the photovoltaic power generation is 20% lower than the predicted value. After analysis, it is found that it is due to thick clouds and insufficient light intensity on that day. In order to make up for this power gap, the following strategy adjustments can be taken: 1. Start the standby small gas generator set to provide additional power support; 2. Adjust the discharge strategy of the energy storage device to increase the discharge power of the energy storage device to meet the load demand; 3. In the subsequent photovoltaic system planning, consider increasing a certain proportion of thin-film photovoltaic panels to improve the power generation capacity of the system under low-light conditions.

[0051] In the present invention, first, according to the power supply area division, configure multiple power supply points, and obtain the environmental data of each power supply point. For the environmental data of the power supply point, predict the wind power and photovoltaic power generation power of each power supply point; then obtain the real-time demand data of the power supply point and predict the change trend of the power demand load; finally, obtain the wind power and photovoltaic power generation power data of the power supply point and the change trend of the power demand load, select the wind-solar-storage equipment, and perform configuration optimization; by respectively predicting the wind power and photovoltaic power generation power of the power supply point in a complex environment and the change trend of the power demand load, select the corresponding wind-solar-storage equipment and perform configuration optimization to improve the energy utilization efficiency and power supply guarantee ability.

[0052] Corresponding to the embodiments of the method for optimizing the configuration of a mobile wind-solar-storage power source in a complex environment described above, the present application also provides embodiments of a device for optimizing the configuration of a mobile wind-solar-storage power source in a complex environment.

[0053] Figure 6 It is a block diagram of a device for optimizing the configuration of a mobile wind-solar-storage power source in a complex environment shown according to an exemplary embodiment. Referring to Figure 6 this, the device may include: a power prediction module 401 for power supply points, a power demand prediction module 402 for power supply points, and a device configuration optimization module 403; wherein: The power prediction module 401 for power supply points is configured to divide the power supply area into multiple power supply points according to the configuration, obtain the environmental data of each power supply point, and predict the wind power and photovoltaic power of each power supply point for the environmental data of the power supply point; The power demand prediction module 402 for power supply points is configured to obtain the real-time demand data of the power supply point and predict the change trend of the power demand load; The device configuration optimization module 403 is configured to obtain the wind power and photovoltaic power data of the power supply point and the change trend of the power demand load, select wind-solar-storage devices, and perform configuration optimization.

[0054] In this embodiment, the power prediction module 401 for power supply points divides the power supply area into multiple power supply points according to the configuration, obtains the environmental data of each power supply point, and predicts the wind power and photovoltaic power of each power supply point for the environmental data of the power supply point; the power demand prediction module 402 for power supply points obtains the real-time demand data of the power supply point and predicts the change trend of the power demand load; the device configuration optimization module 403 obtains the wind power and photovoltaic power data of the power supply point and the change trend of the power demand load, selects wind-solar-storage devices, and performs configuration optimization; by respectively predicting the wind power and photovoltaic power of the power supply point and the change trend of the power demand load in a complex environment, selecting the corresponding wind-solar-storage devices and performing configuration optimization, the energy utilization efficiency and power supply guarantee ability are improved.

[0055] Regarding the device in the above embodiments, the specific manners in which each module performs operations have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0056] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to the descriptions in the method embodiments. The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this application. A person of ordinary skill in the art can understand and implement it without creative work.

[0057] Correspondingly, this application also provides an optimized configuration system for a wind-solar-storage mobile power supply in a complex environment, including: one or more processors; a memory for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the optimized configuration method for a wind-solar-storage mobile power supply in a complex environment as described above. As Figure 7 shown, it is a hardware structure diagram of an optimized configuration system for a wind-solar-storage mobile power supply in a complex environment provided by an embodiment of the present invention in any device with data processing capabilities. Except for Figure 7 the processors, memory, and network interfaces shown, any device with data processing capabilities where the device in the embodiment is located usually also includes other hardware according to the actual functions of the device with data processing capabilities, which will not be elaborated here.

[0058] Correspondingly, this application also provides a computer-readable storage medium, on which computer instructions are stored, and when the instructions are executed by a processor, the optimized configuration method for a wind-solar-storage mobile power supply in a complex environment as described above is implemented. The computer-readable storage medium can be an internal storage unit of any device with data processing capabilities described in any of the foregoing embodiments, such as a hard disk or memory. The computer-readable storage medium can also be an external storage device, such as a plug-in hard disk, a smart media card (SMC), an SD card, a flash card, etc. equipped on the device. Further, the computer-readable storage medium can also include both an internal storage unit and an external storage device of any device with data processing capabilities. The computer-readable storage medium is used to store the computer program and other programs and data required by any device with data processing capabilities, and can also be used to temporarily store the data that has been output or will be output.

[0059] Other embodiments of the present application will be readily contemplated by those skilled in the art upon consideration of the specification and practice of the disclosure herein. The present application is intended to cover any variations, uses, or adaptations of the present application, which follow the general principles of the present application and include known common general knowledge or conventional technical means in the technical field not disclosed in the present application.

[0060] It should be understood that the present application is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope.

Claims

1. An optimization configuration method for a wind-solar-storage mobile power source in a complex environment, characterized in that, It includes the following steps: Divide and configure multiple power supply points according to the power supply area, obtain the environmental data of each power supply point, and predict the wind power generation power and photovoltaic power generation power of each power supply point for the environmental data of the power supply point; Obtain the real-time demand data of the power supply point and predict the change trend of the power demand load; Obtain the wind power generation power, photovoltaic power generation power data and the change trend of the power demand load of the power supply point, select the wind-solar-storage equipment, and perform configuration optimization.

2. The optimized configuration method of the wind-solar-storage mobile power supply in a complex environment according to claim 1, wherein, In the step of dividing and configuring multiple power supply points according to the power supply area, obtaining the environmental data of each power supply point, and predicting the wind power generation power and photovoltaic power generation power of each power supply point for the environmental data of the power supply point: Divide the power supply area into multiple sub-areas according to the geographical features, set multiple power supply points in each sub-area, and set sensors to obtain the environmental data of the power supply point; among them, the environmental data includes wind power, light, and temperature data; Perform data processing on the environmental data and extract key features; among them, the data processing methods include data cleaning, filtering, normalization, and outlier removal, and the key features include wind speed volatility and light attenuation trend; Obtain the wind speed volatility feature and predict the wind power generation power of the power supply point; Obtain the light attenuation trend feature and predict the photovoltaic power generation power of the power supply point.

3. The optimized configuration method of the wind-solar-storage mobile power supply in a complex environment according to claim 2, characterized in that, In the step of obtaining the wind speed volatility feature and predicting the wind power generation power of the power supply point: Collect historical wind speed data and the corresponding wind power generation power data to train the wind power generation power prediction model, obtain the wind speed volatility feature, perform the prediction of the wind power generation power, and output the prediction result.

4. The optimized configuration method of the wind-solar-storage mobile power supply in a complex environment according to claim 2, wherein, In the step of obtaining the light attenuation trend feature and predicting the photovoltaic power generation power of the power supply point: Collect historical light data and the corresponding light attenuation trend data to train the photovoltaic power generation power prediction model, obtain the light attenuation trend feature, perform the prediction of the photovoltaic power generation power, and output the prediction result.

5. The method for optimizing the configuration of a mobile power supply for wind-solar energy storage in a complex environment according to claim 1, characterized in that, In the step of obtaining the real-time demand data of the power supply point and predicting the change trend of the power demand load: Collect the load data of the power supply point; among them, the load data includes load power, current, and voltage data; Obtain the historical electricity consumption data of the power supply point and identify the load usage of the power supply point; Respectively obtain the load data and load usage of the power supply point and predict the change trend of the power demand load.

6. The method for optimizing the configuration of a mobile power source for wind-solar energy storage in a complex environment according to claim 5, wherein In the step of respectively obtaining the load data and load usage of the power supply point and predicting the change trend of the power demand load: Extract the load power, current, voltage, and the average value, maximum value, minimum value, and change rate of the load power within the historical time from the load data and load usage as features, construct a load change trend prediction model, and input the real-time collected load data and the extracted features into the load change trend prediction model to predict the change trend of the demand load.

7. The optimized configuration method of the wind-solar-storage mobile power supply in a complex environment according to claim 1, characterized in that In the step of obtaining the wind power generation power, photovoltaic power generation power data and the change trend of the power demand load of the power supply point, selecting the wind-solar-storage equipment, and performing configuration optimization: Select the model of the power generation equipment with a rated power match according to the wind power generation power prediction result; Select the type of photovoltaic panel according to the photovoltaic power prediction and the installation area; among them, the types of photovoltaic panels include monocrystalline silicon, polycrystalline silicon, and thin-film photovoltaic panels; Select the energy storage equipment according to the load fluctuation amplitude and the intermittency of power generation.

8. The method for optimizing the configuration of a mobile power supply for wind-solar-storage in a complex environment according to claim 7, wherein In the steps of obtaining the wind power and photovoltaic power data of the power supply point and the change trend of the power demand load, selecting the wind-solar-storage equipment, and performing configuration optimization: Monitor the operating states of the power generation equipment, photovoltaic panels, and energy storage equipment respectively, compare the deviations between the actual power generation power, load demand and the predicted values, and dynamically adjust the configuration strategy according to the deviation data.

9. An optimization configuration device for a wind-solar-storage mobile power source in a complex environment, which is applied to the optimization configuration method for a wind-solar-storage mobile power source in a complex environment as described in claim 1, and is characterized in that, It includes a power prediction module for the power supply point, a power demand prediction module for the power supply point, and an equipment configuration optimization module; among them: The power prediction module for the power supply point is used to divide the power supply area into multiple power supply points according to the configuration area, obtain the environmental data of each power supply point, and predict the wind power and photovoltaic power of each power supply point for the environmental data of the power supply point; The power demand prediction module for the power supply point is used to obtain the real-time demand data of the power supply point and predict the change trend of the power demand load; The equipment configuration optimization module is used to obtain the wind power and photovoltaic power data of the power supply point and the change trend of the power demand load, select the wind-solar-storage equipment, and perform configuration optimization.

10. An optimized configuration system for a wind-solar-storage mobile power supply in a complex environment, characterized in that, It includes a processor, a network interface, and a memory. The processor, the network interface, and the memory are interconnected. Among them, the memory is used to store computer programs. The computer programs include program instructions. The processor is configured to call the program instructions to execute the optimization configuration method of the wind-solar-storage mobile power supply in the complex environment according to any one of claims 1 to 8.

Images