Power Dispatching Method, System and Storage Medium of Microgrid Based on Internet of Things

Abstract

This application relates to the technical field of microgrids, and discloses a power dispatching method, system and storage medium for a microgrid based on the Internet of Things. The method first obtains regional images through the cameras of the solar power station, extracts light source and occlusion features, establishes a virtual map and marks relevant features, matches the light source route, calculates the occlusion route, and then obtains the projection route and projection area, and calculates the light energy power generation data of the solar power station. Then, it obtains the real-time power generation power of the wind power station, the current temperature and power of the energy storage station, and the mass energy power generation data of the biomass power generation station, and calculates the comprehensive power generation data. Finally, according to the difference between the comprehensive power generation data and the preset power generation reference data, the mass energy power generation data is adjusted. This method can improve the power quality of the microgrid and optimize power dispatching.

Description

Technical Field

[0001] The present application relates to the technical field of microgrids, and in particular, to a power dispatching method, system, and storage medium for a microgrid based on the Internet of Things. Background Art

[0002] A microgrid is a small power generation and distribution system composed of distributed power sources, energy storage devices, energy conversion devices, related loads, and monitoring and protection devices. It can operate either in parallel with the external power grid or independently.

[0003] A microgrid includes distributed power sources, energy storage devices, energy conversion devices, loads, and monitoring and protection devices. Distributed power sources include solar photovoltaics, wind power generation, small hydropower, biomass power generation, micro gas turbines, etc. These power sources can convert different forms of energy into electrical energy to provide power supply for the microgrid. Energy storage devices such as batteries, supercapacitors, flywheel energy storage, etc. The energy storage device can store energy when the power is excessive and release energy when the power is insufficient, playing a role in suppressing power fluctuations and improving power supply reliability and stability. The energy conversion device mainly includes power electronic converters, etc., which are used to realize the conversion between electrical energies of different voltage levels and frequencies, as well as the control of distributed power sources and energy storage devices, so that they can cooperate effectively with other parts in the microgrid. Loads cover various types of electrical equipment, such as residential electricity, commercial electricity, industrial electricity, etc. According to different importance, loads can be divided into critical loads and non-critical loads, and the microgrid will give priority to ensuring the power supply of critical loads. The monitoring and protection device is used to monitor the operating state of the microgrid in real time, including parameters such as voltage, current, frequency, power, etc., and to dispatch and manage the entire microgrid through the monitoring system. At the same time, the protection device can quickly cut off the circuit in case of a fault to ensure the safety of the microgrid equipment and personnel.

[0004] However, the existing microgrid is dispatched according to the power fluctuations of different distributed power sources. During dispatching, a large amplitude of power fluctuation has already occurred, and at this time, the power quality has dropped to a large extent. Therefore, a method that can perform power dispatching in a timely manner and alleviate the decline of power quality is needed. Summary of the Invention

[0005] In order to improve the power quality of the microgrid during the dispatching process, the present application provides a power dispatching method, system, and storage medium for a microgrid based on the Internet of Things.

[0006] In the first aspect, the present application provides a power dispatching method for a microgrid based on the Internet of Things, adopting the following technical solution:

[0007] A power dispatching method for a microgrid based on the Internet of Things includes the following steps:

[0008] The camera based on the solar power station acquires the regional image in the set area;

[0009] Extract the light source feature and the occlusion feature from the regional image;

[0010] Based on the coordinates of the solar power station, establish a virtual map according to the regional image, and mark the light source feature and the occlusion feature from the virtual map;

[0011] Match the light source route corresponding to the light source feature from the preset database;

[0012] Calculate the moving route of the occlusion feature as the occlusion route within the time period of the nearest preset duration;

[0013] Within the same time axis, calculate the projection route based on the light source feature, the light source route, the occlusion feature and the occlusion route; the projection route is the route of the occlusion feature projected on the solar power station, including the passed route and the unpassed route; the passed route is the actual route, and the unpassed route is the predicted route; the predicted route extends from the end according to the change trend of the end of the actual route;

[0014] Calculate the projected area of the predicted route on the solar power station;

[0015] Calculate the light energy power generation data of the solar power station = 1 - the projected area / the total area of the solar power station;

[0016] Obtain the real-time power generation power of the wind power station, and match the wind energy power generation data of the wind power station from the preset wind power database;

[0017] Obtain the current temperature and current power of the energy storage station, and match the energy storage power generation data of the energy storage station from the preset energy storage database based on the current temperature;

[0018] Obtain the mass energy power generation data of the biomass power station;

[0019] Calculate the comprehensive power generation data according to the light energy power generation data, the wind energy power generation data and the energy storage power generation data;

[0020] If the difference between the comprehensive power generation data and the preset power generation reference data adjusts the mass energy power generation data, the greater the difference, the greater the mass energy power generation data, and the smaller the difference, the smaller the mass energy power generation data.

[0021] By adopting the above technical solution, by calculating the data of each power generation station in advance and adjusting the mass-energy power generation data according to the difference between the comprehensive power generation data and the preset reference data, scheduling adjustment can be carried out before power fluctuations, effectively alleviating the decline in power quality. Compared with the traditional method of scheduling only after a large power fluctuation occurs, the power quality of the microgrid during the scheduling process can be significantly improved. By using the camera of the solar power station to obtain and analyze images, the impact of the shading feature (cloud layer) on solar power generation can be accurately predicted, and power scheduling can be planned in advance. At the same time, by combining the data of the wind power station, energy storage station and biomass power generation station, collaborative optimization scheduling of multiple power sources can be realized, improving the reliability and stability of the microgrid power supply. By comprehensively considering the power generation capabilities of various distributed power sources and flexibly adjusting the power generation strategy according to the real-time conditions of different energy sources, various energy sources can be fully utilized in the microgrid, improving energy utilization efficiency and reducing energy waste.

[0022] Optionally, the method for adjusting the mass-energy power generation data further includes the following steps:

[0023] Based on the projection route, calculate the predicted distance between the shading feature and the light source feature within a future set time period;

[0024] Inversely adjust the increment of the mass-energy power generation data according to the predicted distance. The larger the predicted distance, the smaller the increment of the mass-energy power generation data; the smaller the predicted distance, the larger the increment of the mass-energy power generation data.

[0025] By adopting the above technical solution, by calculating the predicted distance between the shading feature and the light source feature within a future set time period to adjust the increment of the mass-energy power generation data, on the basis of the original adjustment according to the distance between the shading feature (cloud) and the light source feature (sun), predictive adjustment of future situations is added. It can not only cope with the current degree of influence on solar energy, but also make advance arrangements, making power scheduling more forward-looking in the time dimension and more accurately matching the real-time power demand of the microgrid, continuously improving power quality. Inversely adjusting the increment of the mass-energy power generation data according to the predicted distance, when it is predicted that the degree of influence on solar energy is small, the increment of biomass power generation is reduced in advance to avoid overproduction and waste of energy; when it is predicted that the influence on solar energy is large, the increment of biomass power generation is increased in advance to timely fill the future power gap, making the energy utilization of the microgrid more reasonable in the time span and further reducing energy costs. Through the advance adjustment strategy for future situations, when the microgrid faces potential fluctuations in solar power generation, it can make preparations for power supply adjustment in advance, greatly reducing problems such as large fluctuations in voltage and frequency caused by sudden changes in power supply. In a complex and changeable power generation environment, the stability and reliability of the microgrid operation are ensured, providing a more stable and reliable power supply for various loads and meeting the electricity consumption needs of critical and non-critical loads at different times.

[0026] Optionally, the method for calculating the projected area of the predicted route on the solar power station further includes the following steps:

[0027] Obtain all solar street lamp data within the preset range of the solar power station. The solar street lamp data includes location information and power information. Use each piece of solar street lamp data as an element to construct a temporary power detection array, where the location information is used as the element position and the power information is used as the element information.

[0028] Extract the power change characteristics based on the element positions and element information in the temporary power detection array.

[0029] Calculate the moving trend and deformation trend of the power change characteristics in the temporary power detection array within a preset time range.

[0030] Obtain the light energy influence trend through weighted calculation based on the moving trend and deformation trend.

[0031] Correct the projected area according to the light energy influence trend. The greater the light energy influence trend, the larger the projected area; the smaller the light energy influence trend, the smaller the projected area.

[0032] By adopting the above technical solution, a temporary power detection array is constructed by obtaining solar street lamp data. The power change characteristics are extracted from the array and its moving trend and deformation trend are analyzed, and then the light energy influence trend is obtained to correct the projected area. This method comprehensively considers the actual light change situation around the solar power station. Compared with simply relying on camera image analysis, it can more comprehensively and accurately reflect the degree of solar energy affected, making the calculation of the projected area more in line with the actual situation and laying a foundation for accurately calculating the light energy power generation data in the future. Using solar street lamp data, the light conditions in a small area around the solar power station are taken into account, no longer limited to only analyzing the sky area image obtained from the solar power station camera. This makes the evaluation of solar power generation more comprehensive, considering the impact of local area light differences on power generation, and can more accurately grasp the state of solar power generation in the entire microgrid, so as to make more reasonable decisions during power dispatching, improving the comprehensiveness and reliability of microgrid power dispatching. Real-time analysis of the moving trend and deformation trend of the power change characteristics of solar street lamps can timely capture the dynamic changes of the light environment. Correcting the projected area according to the light energy influence trend obtained from these dynamic changes enables the power dispatching system to adaptively adjust according to the real-time changes of the actual light environment, enhancing the flexibility and adaptability of microgrid power dispatching under different light conditions and ensuring the stable operation of the microgrid.

[0033] Optionally, the method for calculating the projected area of the predicted route on the solar power station further includes the following sub-steps:

[0034] According to the vector corresponding to the movement trend Correct the actual route For ,

[0035] Correct formula A to:

[0036] ; where is the adjustment coefficient;

[0037] According to the deformation trend Correct the shortest distance between the predicted route and the solar power station For ,

[0038] Correct formula B to:

[0039] .

[0040] By adopting the above technical solution, using formula A to correct the actual route according to the movement trend can make the determination of the actual route more conform to the real movement trajectory of cloud and other shielding features. The movement trend reflects the movement direction and speed change of the shielding feature within a certain period of time. By quantitatively correcting it through formula A, the problem of inaccurate actual route caused by initial calculation deviation is avoided, so as to more accurately grasp the real-time influence path of clouds and other on solar power generation, and provide guarantee for subsequent accurate calculation of the projection route and area. Using the deformation trend combined with formula B to correct the shortest distance between the predicted route and the solar power station takes into account the potential difference in the influence of the shape change of the shielding feature during movement on solar power generation. For example, clouds may stretch or compress during movement, and the shortest distance between them and the solar power station will change accordingly, and the degree of influence on solar power generation is also different. Through this correction, the degree of influence of future solar power generation can be predicted more accurately, and more accurate preparations can be made in advance for power dispatching.

[0041] Optionally, the method for calculating the projected area of the predicted route on the solar power station further includes the following steps:

[0042] Calculate the pixel ratio of the shielding feature in the regional image. If the pixel ratio is greater than the set value, obtain real-time wind speed data based on a preset wind speed detection device;

[0043] Match the wind power overflow data according to the wind speed data and the real-time power generation power;

[0044] If the wind power overflow data is greater than the preset overflow reference data, correct the projected area according to the wind power overflow data. The larger the wind power overflow data, the larger the projected area, and the smaller the wind power overflow data, the smaller the projected area.

[0045] By adopting the above technical solution, calculating the proportion of occluded feature pixels can intuitively determine the sky cloud coverage. When there are more clouds, wind speed data is obtained by combining with a wind speed detection device, and then wind power spillage data is matched according to the wind speed and the real-time power generation to correct the projected area. This series of operations comprehensively consider the meteorological factors affecting solar power generation. Compared with relying only on image analysis, it more accurately evaluates the impact of cloud movement on the illumination of the solar station, makes the calculation of the projected area closer to the actual situation, and provides strong support for accurately calculating the light energy power generation data. By analyzing the wind power spillage data, the relationship between wind power and power generation can be deeply understood. When the wind power spillage data is greater than the reference value, the projected area is corrected, and the estimation of the solar power generation capacity can be adjusted in a timely manner. This provides more comprehensive and accurate information for power dispatching. The dispatching system can rationally allocate the power generation of each power station based on more accurate light energy power generation data, optimize the power dispatching decision, improve the power quality and power supply stability of the microgrid. Considering the influence of wind speed and wind power on cloud movement enables the power dispatching system to better adapt to complex and changeable meteorological conditions. Under different wind power and cloud conditions, the evaluation of solar power generation can be dynamically adjusted, enhancing the ability of the microgrid to cope with complex meteorological environments, ensuring stable and reliable power supply to various loads in various weather conditions, and improving the reliability and adaptability of the microgrid operation.

[0046] Optionally, the preheating method of the energy storage station includes the following steps:

[0047] When the current temperature is lower than a preset reference temperature and the wind power spillage data is greater than a preset spillage reference data;

[0048] Based on the historical corresponding relationship between the wind power spillage data and temperature change, the power output influencing factors of the energy storage station are matched;

[0049] Adjust the preheating power of the energy storage station according to the power output influencing factors. The greater the power output influencing factor, the greater the preheating power; the smaller the power output influencing factor, the smaller the preheating power.

[0050] By adopting the above technical solution, in the case of abnormal low-temperature and wind power overflow data, the influencing factors of power output are matched based on historical data, and then the preheating power of the energy storage station is adjusted. Precise regulation according to the magnitude of the influencing factors can effectively compensate for the negative impact of environmental factors on the performance of the energy storage station, ensure the stable operation of the energy storage station under complex working conditions, and continuously play the functions of energy storage and power supply regulation. The energy storage station is a key link for stable power supply in the microgrid. By reasonably adjusting the preheating power to ensure the normal power output of the energy storage station in a harsh environment, it can effectively reduce the power fluctuations in the microgrid caused by the performance degradation of the energy storage station, and greatly improve the stability and reliability of power supply to various loads in the microgrid. Adjusting the preheating power according to the magnitude of the power output influencing factors avoids waste or insufficient input of energy. It can not only meet the requirements for improving the performance of the energy storage station, but also maximize the energy utilization efficiency and reduce the energy cost of microgrid operation.

[0051] Optionally, the method for the charging priority of the energy storage station includes the following steps:

[0052] Obtain the solar power generation power of the solar power station. When the solar power generation power of the solar power station is greater than the set light energy start threshold, it is determined that solar energy is in an available state, and the control switch is controlled to connect the solar power generation to the charging circuit of the energy storage station;

[0053] If the solar power generation power is lower than the set light energy lower limit threshold, detect the states of wind energy and biomass energy; if the real-time power generation power of the wind power station is set to the wind energy start value, control the switch to connect the wind energy to the charging circuit of the energy storage station to replace solar energy to charge the energy storage station;

[0054] When the real-time power generation power of the wind power station is lower than the set wind energy lower limit threshold, judge the biomass energy state; if the biomass energy generation data is greater than the set biomass energy start threshold, control the switch to connect the biomass energy to the charging circuit of the energy storage station to replace the wind energy to charge the energy storage station.

[0055] By adopting the above technical solution, the charging sequence of the battery is controlled, with solar energy taking precedence, followed by wind energy and biomass energy, and biomass energy giving priority to powering the load. By setting the startup threshold and lower limit threshold of different energy sources, solar power generation is preferentially used to charge the energy storage station. When the solar power generation is insufficient, it is sequentially switched to wind energy and biomass energy to ensure that each energy source can be fully utilized when its power generation meets the conditions, avoiding energy waste and improving the energy utilization efficiency of the entire microgrid system. The orderly switching among solar energy, wind energy, and biomass energy makes the charging process of the energy storage station more stable and reliable. As a key link in the microgrid, the stable charging state of the energy storage station helps to maintain the power balance of the microgrid. In the case of power generation fluctuations of different energy sources, it can still provide continuous and stable power supply to the load, enhancing the ability of the microgrid to cope with energy fluctuations. Reasonably using multiple energy sources for charging reduces the dependence on a single energy source and avoids additional energy procurement costs caused by insufficient supply of a certain energy source. At the same time, improving energy utilization efficiency also indirectly reduces the operating cost of the microgrid and improves economic benefits.

[0056] In a second aspect, the present application provides a power dispatching system for an Internet of Things-based microgrid, adopting the following technical solution:

[0057] A power dispatching system for an Internet of Things-based microgrid includes a processor, and the processor executes the steps of the power dispatching method for an Internet of Things-based microgrid described in any one of the above.

[0058] In a third aspect, the present application provides a storage medium, adopting the following technical solution:

[0059] A storage medium stores a program, and when the program is executed by a processor, it implements the steps of the power dispatching method for an Internet of Things-based microgrid described in any one of the above.

[0060] In summary, the present application includes at least one of the following beneficial technical effects: For the power dispatching method of the Internet of Things-based microgrid, first, images are obtained through the camera of the solar station, the light energy power generation data is analyzed and calculated, and the comprehensive power generation data is calculated by combining the data of the wind power station, the energy storage station, and the biomass power generation station. Then, the biomass power generation data is adjusted according to the difference between it and the preset value. At the same time, it is optimized from multiple aspects: the biomass power generation increment is adjusted according to the predicted distance of the projection route; the projection area is corrected using the data of solar street lights; the distance between the actual route and the predicted route is corrected by a formula; the projection area is corrected considering the wind speed and the proportion of cloud pixels; the preheating power of the energy storage station is adjusted according to environmental factors; the charging of the energy storage station is controlled according to the energy priority. These measures can accurately predict and schedule in advance, improve the power quality and power supply reliability, improve the energy utilization efficiency, reduce costs, and enhance the ability of the microgrid to cope with complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 It is a step diagram of a power dispatching method for an Internet of Things-based microgrid.

[0062] Figure 2 It is a step diagram included in the method for calculating the projected area of the predicted route on the solar power station.

[0063] Figure 3 It is also a step diagram included in the method for calculating the projected area of the predicted route on the solar power station.

[0064] Figure 4 It is a step diagram of a preheating method for an energy storage station. Detailed implementation manners

[0065] The following details the implementation manners of the present application, and the examples of the implementation manners are shown in the accompanying drawings.

[0066] In the description of this specification, the description with reference to the terms "certain implementation manners", "one implementation manner", "some implementation manners", "illustrative implementation manners", "examples", "specific examples" or "some examples" means that the specific features, structures, materials or characteristics described in connection with the implementation manner or example are included in at least one implementation manner or example of the present application. In this specification, the schematic expressions of the above terms do not necessarily refer to the same implementation manner or example. Moreover, the specific features, structures, materials or characteristics described can be combined in a suitable manner in any one or more implementation manners or examples.

[0067] The embodiments of the present application disclose a power dispatching method for an Internet of Things-based microgrid. Referring to Figure 1 , the method includes the following steps:

[0068] Deploy high-definition cameras at the solar power station, and use Internet of Things technology to obtain real-time regional images in a set area. The regional images contain rich sky information, such as blue sky and white clouds. Through advanced image recognition algorithms, light source features (mainly sun-related features) and occlusion features (such as clouds, etc.) are accurately extracted from the regional images, and these features are key elements for evaluating solar power generation.

[0069] Based on the precise coordinate information of the solar power station, with the help of Geographic Information System (GIS) technology, a virtual map is constructed according to the acquired regional images. In the virtual map, the extracted light source features and occlusion features are clearly marked to achieve a visual representation of the lighting environment around the solar power station. At the same time, from a preset database, using a matching algorithm, the light source routes corresponding to the light source features are quickly matched. This database stores a large amount of data on the running trajectories of the sun at different times, seasons, and geographical locations. Within the recently preset time period (such as 5 - 10 minutes), using a motion analysis algorithm, the moving route of the occlusion feature is calculated, defined as the occlusion route, to track the moving trajectories of occluding objects such as clouds.

[0070] Within the same time axis, based on the light source features, light source routes, occlusion features, and occlusion routes, a complex geometric calculation model is used to calculate the projection route. This projection route is the route of the occlusion feature projected on the solar power station, where the passed route is the actual route, determined by the actual monitoring data of the occlusion projection in the past time period; the unpassed route is the predicted route, which extends from the end based on the change trend at the end of the actual route using the trend extrapolation algorithm. Then, the projected area of the predicted route on the solar power station is accurately calculated, and the size of this area is directly related to the degree of influence on solar power generation.

[0071] According to the calculated projected area, through the formula "Light energy power generation data of the solar power station = 1 - Projected area / Total area of the solar power station", the light energy power generation data of the solar power station is calculated, which intuitively reflects the real-time state of solar power generation. At the same time, the real-time power generation power of the wind power station is obtained through IoT sensors. Based on this power data, the wind energy power generation data of the wind power station is matched from a preset wind power database (storing power generation data under different wind speeds, wind directions, etc.). The current temperature and current power of the energy storage station are obtained, and using the energy storage characteristic model, the energy storage power generation data of the energy storage station is matched from a preset energy storage database based on the current temperature. The mass - energy power generation data of the biomass power station is obtained, which reflects the real - time power generation capacity of the biomass power station. Finally, according to the light energy power generation data, wind energy power generation data, and energy storage power generation data, using a weighted comprehensive calculation model, the comprehensive power generation data is calculated to comprehensively evaluate the current power generation capacity of the micro - grid.

[0072] The calculated comprehensive power generation data is compared with the preset power generation reference data. If there is a difference, the mass - energy power generation data is adjusted according to the size of the difference. The larger the difference, the larger the mass - energy power generation data, and the smaller the difference, the smaller the mass - energy power generation data. Through this dynamic adjustment mechanism, precise control of the micro - grid power generation is achieved.

[0073] Suppose at a certain moment, the preset power generation reference data is 1000 kW, and the calculated comprehensive power generation data is 800 kW. At this time, the difference is 200 kW. Since the difference is relatively large, according to the rule that "the larger the difference, the larger the mass-energy power generation data", the biomass power plant needs to increase its power generation. For example, the original mass-energy power generation data of the biomass power plant is 100 kW, and now it is adjusted to 300 kW, for instance, to make up for the gap between the comprehensive power generation data and the reference data and make the overall power generation situation closer to the preset value.

[0074] For another example, at another moment, the preset power generation reference data is still 1000 kW, and the calculated comprehensive power generation data is 950 kW, with a difference of 50 kW. Because the difference is relatively small, according to the rule, the biomass power plant does not need to significantly adjust its power generation; if the original mass-energy power generation data is 150 kW, it is now appropriately adjusted to 170 kW, and through fine-tuning, the comprehensive power generation data is made more in line with the preset power generation reference data, so as to achieve precise control of the microgrid power generation and ensure the stable operation of the microgrid and the balance of power supply.

[0075] By calculating the data of each power plant in advance and adjusting the mass-energy power generation data according to the difference between the comprehensive power generation data and the preset reference data, scheduling adjustments can be made before power fluctuations occur, effectively alleviating the decline in power quality. Compared with the traditional method of scheduling only after large power fluctuations occur, it can significantly improve the power quality of the microgrid during the scheduling process and reduce equipment damage and production interruption caused by power quality problems. By using the camera of the solar power station to obtain and analyze images, the impact of shading features (clouds) on solar power generation can be accurately predicted, and power scheduling can be planned in advance. At the same time, by combining the data of the wind power station, energy storage station and biomass power station, multi-source collaborative optimization scheduling can be realized, improving the reliability and stability of the microgrid power supply and ensuring the continuous and stable power consumption of various loads. By comprehensively considering the power generation capabilities of various distributed power sources and flexibly adjusting the power generation strategy according to the real-time situation of different energy sources, various energy sources can be fully utilized in the microgrid, improving energy utilization efficiency, reducing energy waste, reducing dependence on a single energy source, and enhancing the economic and environmental benefits of the microgrid.

[0076] To further optimize the power scheduling of the microgrid, improve its operation stability and energy utilization efficiency, the method of adjusting the mass-energy power generation data further includes the following steps:

[0077] First, based on the previously calculated projection route, using advanced trajectory prediction algorithms and time series analysis techniques, calculate the predicted distance between the occlusion features (mainly referring to clouds) and the light source features (the sun) within a set future time period (e.g., the next 1 - 2 hours). During the calculation process, fully consider the moving speed and direction changes of the clouds as well as the sun's trajectory in the sky. These data are obtained through high-precision sensors and real-time monitoring systems. For example, with the help of the cloud dynamic data provided by meteorological satellites and the sun azimuth monitoring devices deployed at solar power stations, ensure the accuracy of the predicted data.

[0078] Then, inversely and correlatively adjust the increment of the mass-energy power generation data according to the predicted distance, and establish a mathematical model to quantify the relationship between the predicted distance and the increment of the mass-energy power generation data. Specifically, the larger the predicted distance, the smaller the impact of the clouds on solar power generation in the future time period, and at this time, the smaller the increment of the mass-energy power generation data; the smaller the predicted distance, the greater the possibility of the clouds blocking solar power generation, and the greater the increment of the mass-energy power generation data. Through this inversely correlative adjustment mechanism, achieve precise control of the power generation of the biomass power station.

[0079] Suppose the established mathematical model is: Increment of mass-energy power generation data = 1000 / Predicted distance (unit: kilometers), where 1000 is a coefficient determined comprehensively according to the power generation characteristics of the microgrid, historical data, and power generation demand.

[0080] If the predicted distance is 5 kilometers, calculated according to the formula, the increment of mass-energy power generation data = 1000 / 5 = 200 kilowatts. This means that at the current predicted distance, the biomass power station needs to increase the power generation by 200 kilowatts to cope with the possible reduction in solar power generation.

[0081] When the predicted distance increases to 10 kilometers, the increment of mass-energy power generation data = 1000 / 10 = 100 kilowatts. As the predicted distance increases, the impact of the clouds on solar power generation becomes smaller, so the increment of the mass-energy power generation data decreases accordingly, and the biomass power station only needs to increase the power generation by 100 kilowatts.

[0082] Suppose again that the predicted distance decreases to 2 kilometers. At this time, the increment of mass-energy power generation data = 1000 / 2 = 500 kilowatts. Since the predicted distance becomes smaller, the possibility of the clouds blocking solar power generation increases, so the increment of the mass-energy power generation data increases significantly, and the biomass power station needs to increase the power generation by 500 kilowatts to ensure the stable power supply of the microgrid.

[0083] On the basis of the original real-time adjustment according to the distance between the shielding feature (cloud) and the light source feature (sun), predictive adjustment for future situations is added. In the past, scheduling was only based on the current distance between the cloud layer and the sun, which often had a lag and was difficult to cope with rapidly changing weather conditions. Now, by predicting the future distance, not only can the current impact on solar energy be addressed, but also advance planning can be carried out, making the power dispatching more forward-looking in the time dimension. For example, when it is predicted that the cloud layer will gradually move away from the sun in the future and the impact on solar power generation is relatively small, the increment of biomass power generation can be reduced in advance to avoid overproduction and waste of energy, and the excess biomass raw materials can be stored for use when more needed later. Conversely, when it is predicted that the impact on solar energy is relatively large, the increment of biomass power generation can be increased in advance to timely fill the future power gap and ensure the stable power supply of the microgrid. This makes the energy utilization of the microgrid more reasonable in terms of time span, further reduces the energy cost, and improves the economic benefits of the entire microgrid system.

[0084] From the perspective of the operation stability of the microgrid, through the advance adjustment strategy for future situations, the microgrid can be prepared in advance for power supply adjustment when facing potential fluctuations in solar power generation. When a possible cloud shielding situation is detected, the biomass power generation is increased in advance to avoid sudden changes in power supply caused by a sudden reduction in solar power generation, greatly reducing problems such as large fluctuations in voltage and frequency caused by sudden changes in power supply. In a complex and changeable power generation environment, whether it is a sudden cloud shielding on a sunny day or fluctuations in solar power generation caused by gradually changing weather, the operation stability and reliability of the microgrid can be ensured. This provides a more stable and reliable power supply for various loads, meeting the power consumption needs of critical loads (such as important places that cannot be powered off, like hospitals and financial institutions) and non-critical loads (such as ordinary residential electricity and some commercial electricity) at different times, ensuring the normal operation of the entire social production and life.

[0085] Refer to Figure 2 , in the process of accurately evaluating solar power generation and optimizing the power dispatching of the microgrid, calculating the projected area of the prediction route on the solar power station is a crucial step. By introducing solar street lamp data, the optimization method for calculating the projected area of the prediction route on the solar power station includes the following steps:

[0086] With the help of Internet of Things technology, obtain all the solar street lamp data within the preset range of the solar power station (such as within the area centered on the solar power station with a radius of 5 - 10 kilometers). These solar street lamp data cover location information (determined by GPS positioning or high-precision geographic information system) and power information (real-time collected by the power sensors built in the street lamps). Take each solar street lamp data as an element to construct a temporary power detection array. In this array, the location information defines the spatial position of the element, and the power information serves as the key attribute information of the element. In this way, the solar street lamp data around the solar power station are integrated and structured, providing an ordered data basis for subsequent analysis.

[0087] Apply data analysis algorithms to deeply mine the element positions and element information in the temporary power detection array, and extract power change characteristics. For example, analyze the rate of change of the power of solar street lamps at different positions over time, and the power differences between adjacent street lamps. Through these characteristics, the change situation of the light intensity in different regions and the spatial distribution characteristics of the light change can be intuitively reflected. These characteristics are important bases for judging the affected status of solar energy, and can reveal the dynamic changes of the lighting environment more than just the simple power values.

[0088] Within the preset time range (such as every 15 - 30 minutes as an analysis period), calculate the moving trend and deformation trend of the extracted power change characteristics in the temporary power detection array. The moving trend mainly focuses on the moving direction and speed of the power change characteristics in the spatial position. For example, whether the light intensity weakening characteristic in a certain area is approaching the solar power station and what the moving speed is. The deformation trend focuses on analyzing the changes in the form of the power change characteristics, such as whether the range of the light intensity change is expanding or shrinking, and whether the change gradient has changed, etc. Through the accurate calculation of these trends, a more comprehensive understanding of the dynamic evolution process of the lighting environment can be obtained.

[0089] According to the moving trend and deformation trend, use a weighted calculation model to obtain the light energy influence trend. In this model, different weights are assigned to the moving trend and deformation trend, and the determination of the weights is based on the analysis of a large amount of historical data and the actual lighting environment. For example, in areas where cloud movements are relatively frequent, the weight of the moving trend may be relatively high; while in scenarios where the light changes are more complex and the form changes are diverse, the weight of the deformation trend will be appropriately increased. Through reasonable weighted calculation, a light energy influence trend value that can comprehensively reflect the degree of influence of the lighting environment change on solar power generation is obtained.

[0090] Correct the projected area according to the trend of light energy influence, and establish a clear correlation between the two: the greater the trend of light energy influence, the greater the negative impact of the lighting environment on solar power generation, and at this time the projected area is larger; the smaller the trend of light energy influence, the smaller the impact of the lighting environment on solar power generation, and the smaller the projected area. Through this correction method, the calculation of the projected area is more in line with the actual lighting situation.

[0091] Suppose the initially calculated projected area is 100 square meters, and the range of the light energy influence trend is 0 - 10. The larger the value, the greater the negative impact of the lighting environment on solar power generation. When the light energy influence trend is 2, it indicates that the lighting environment has a small impact on solar power generation. According to the correction rule, the projected area needs to be reduced accordingly. Suppose the projected area is adjusted to 100×(1 - 0.2) = 80 square meters according to the proportional relationship. This is because a lower light energy influence trend means that the interference of clouds and other obstacles to light is weak, and the actual area blocking the solar station is also small.

[0092] When the light energy influence trend increases to 8, it shows that the lighting environment has a large negative impact on solar power generation, and at this time the projected area should increase. According to the correction rule, the projected area becomes 100×(1 + 0.8) = 180 square meters. Because a higher light energy influence trend represents more clouds and other obstacles, a wider range of the solar station is blocked, and the obstruction to solar power generation is greater, so the projected area increases, which is more in line with the actual lighting situation.

[0093] Compared with the traditional method of calculating the projected area only by camera image analysis, this method has significant advantages. By obtaining solar street lamp data to construct a temporary power detection array, and deeply analyzing the power change characteristics and their trends, and then obtaining the light energy influence trend to correct the projected area, it comprehensively considers the actual lighting changes around the solar station. As a device that directly receives light and generates electricity, the power change of solar street lamps directly reflects the change of light intensity. This method can more comprehensively and accurately reflect the degree of solar energy affected than simply relying on camera image analysis. It makes the calculation of the projected area more in line with the actual situation, laying a solid foundation for accurately calculating solar power generation data subsequently.

[0094] This method takes into account the lighting conditions in a small area around the solar power station, rather than being limited to analyzing only the images of the sky area obtained from the cameras of the solar power station. The cameras of the solar power station mainly focus on the shielding conditions such as clouds in the sky, but it is difficult to comprehensively capture the lighting differences in local ground areas. The solar street lights are distributed in the surrounding areas and can sense the lighting changes at different positions, which makes the evaluation of solar power generation more comprehensive. Considering the impact of local lighting differences on power generation, it can more accurately grasp the state of solar power generation in the entire microgrid. During power dispatching, based on a more accurate assessment of the solar power generation state, more reasonable decisions can be made, improving the comprehensiveness and reliability of the microgrid power dispatching.

[0095] By analyzing the moving trend and deformation trend of the power change characteristics of solar street lights in real time, the dynamic changes in the lighting environment can be captured in a timely manner. Whether the lighting change is caused by cloud movement, building occlusion, or other factors, it can be quickly detected. The projection area is corrected according to the light energy influence trend obtained from these dynamic changes, enabling the power dispatching system to adaptively adjust according to the real-time changes in the actual lighting environment. Under different lighting conditions, such as early morning, evening, and cloudy weather, the assessment of solar power generation and the power dispatching strategy can be adjusted in a timely manner, enhancing the flexibility and adaptability of the microgrid power dispatching under different lighting conditions and ensuring the stable operation of the microgrid.

[0096] Correction of the actual route based on the moving trend:

[0097] According to the vector corresponding to the moving trend to correct the actual route. Here, the moving trend is obtained by monitoring and analyzing the movement direction and speed changes of shielding features (such as clouds) over a certain period of time. In the actual meteorological environment, the movement of clouds is not constant and may change due to various factors such as air currents and weather systems. The vector accurately captures this displacement change in space.

[0098] The specific correction formula A is: . Among them, represents the coordinate of a certain point on the initially calculated actual route, while is the corrected coordinate. is a carefully set adjustment coefficient, and its value is not fixed but is dynamically adjusted according to a large amount of historical data and real-time meteorological conditions and other factors. For example, in weather conditions with strong winds, the movement speed of clouds may increase, and at this time The value will be adjusted accordingly to more accurately reflect the actual movement of the cloud layer. Through such a quantitative correction method, the problem of inaccurate actual route caused by initial calculation deviation can be effectively avoided. In previous calculations, due to insufficient consideration of the complexity of cloud movement, there may be a large error between the calculated actual route and the true trajectory. With the help of formula A, the actual route can be dynamically corrected according to the real-time movement trend, so as to more accurately grasp the real-time influence path of the cloud layer on solar power generation. This is like in a navigation system, adjusting the route plan in real time according to the actual driving situation of the vehicle to ensure accurate calculation of the projected route and area subsequently, providing reliable basic data for power dispatching.

[0099] Correction of the shortest distance based on the deformation trend:

[0100] According to the deformation trend Correct the shortest distance between the predicted route and the solar power station. During the movement of the shielding feature, its shape does not remain fixed and may undergo various changes such as stretching, compression, and distortion. This change in shape will directly affect the shortest distance between it and the solar power station, and thus have different degrees of influence on solar power generation. For example, when the cloud layer gradually stretches during movement, its coverage range may expand, and the shortest distance from the solar power station will also change accordingly, and the area and degree of solar energy blockage will also be different.

[0101] The correction formula B is: . Among them, is the shortest distance between the predicted route of the initial calculation and the solar power station, is the shortest distance after correction. is also a coefficient that is dynamically adjusted according to the actual situation, which reflects the degree of deformation of the shielding feature. Through the analysis and research of a large number of cloud deformation cases and combined with real-time meteorological monitoring data, the value of ) can be accurately determined. Using the deformation trend and combining with formula B for correction fully takes into account the potential differences in the influence of the shape change of the shielding feature during movement on solar power generation. Compared with the traditional calculation method, this correction method can more accurately predict the degree of future solar power generation affected. In power dispatching, according to more accurate predictions, the adjustment of the power generation plan can be made in advance, and the power generation of other power sources such as biomass power stations and wind power stations can be reasonably arranged to ensure the stable and reliable power supply of the microgrid and provide continuous and high-quality power services for various loads.

[0102] Refer to Figure 3 , when calculating the projected area of the predicted route on the solar power station, in order to achieve more accurate calculation and evaluation, a series of steps that fully consider meteorological factors are introduced as follows:

[0103] First, use an image analysis algorithm to calculate the pixel proportion of the occlusion feature (mainly referring to clouds) in the regional image. The regional image is obtained in real time by the camera of the solar power station, covering the sky scene within a certain range around the solar power station. By calculating the ratio of the number of pixels representing clouds in the image to the total number of pixels in the entire image, the coverage degree of clouds in the sky can be intuitively judged. For example, if the pixel proportion reaches more than 50%, it can be initially determined that the cloud coverage is relatively dense.

[0104] When the calculated pixel proportion is greater than the set value (this set value can be adjusted according to the actual situation and historical data, such as commonly set at 30%-40%), the process of obtaining real-time wind speed data based on a preset wind speed detection device is initiated. The wind speed detection device usually uses high-precision wind speed sensors, which are installed at appropriate positions around the solar power station to ensure that local wind speed information can be accurately captured. These wind speed sensors are connected to the data processing system through Internet of Things technology and can transmit the collected wind speed data to the system in real time, providing basic data support for subsequent analysis.

[0105] Next, match the wind power overflow data according to the obtained wind speed data and the real-time power generation. The real-time power generation of the wind power station is fed back in real time by its own monitoring system, reflecting the power generation capacity under the current wind conditions. In this matching process, a pre-established mathematical model and database are used. The database stores a large amount of power generation data corresponding to different wind speeds, wind directions, and other meteorological conditions. By comparing and analyzing the real-time wind speed data with the data in the database and combining the real-time power generation of the wind power station, the wind power overflow data is calculated. For example, when the wind speed is relatively high, but the power generation of the wind power station does not reach the expected theoretical value, it indicates that there is a certain amount of wind power that is not fully utilized. The power value corresponding to this part of the wind power that has not been effectively converted into electrical energy is the wind power overflow data.

[0106] If the calculated wind power overflow data is greater than the preset overflow reference data (this reference data can also be set according to the actual situation and historical operation data, such as commonly set at 10%-20% of the rated power of the wind power station), then correct the projection area according to the wind power overflow data. A clear corresponding relationship is established here: the larger the wind power overflow data, the higher the degree of wind power that is not fully utilized, and at the same time, it also means that the clouds may have a greater shading effect on the solar power station under the action of the wind, so the projection area is larger; conversely, the smaller the wind power overflow data, the smaller the projection area.

[0107] Suppose the rated power of the wind power station is 1000 kilowatts, and the preset overflow reference data is set at 15% of the rated power of the wind power station, that is, 150 kilowatts.

[0108] When the wind power spillage data is large: In a certain calculation, the wind power spillage data is 250 kW, which is greater than the spillage reference data of 150 kW. Since the wind power spillage data is large, it indicates a high degree of underutilization of wind power, and the clouds may be more likely to block the solar power station under the action of strong winds. Assuming that the originally preliminarily calculated projected area is 200 square meters, according to the corresponding relationship, the projected area corrected based on the wind power spillage data increases to 300 square meters to reflect the greater shading effect of the clouds on the solar power station under the influence of wind.

[0109] When the wind power spillage data is small: In another calculation, the wind power spillage data is 80 kW, which is less than the spillage reference data of 150 kW. At this time, the degree of underutilization of wind power is low, and the shading effect of the clouds on the solar power station under the action of wind is also small. If the originally preliminarily calculated projected area is also 200 square meters, the projected area after correction decreases to 150 square meters, which conforms to the inverse correlation between the wind power spillage data and the projected area.

[0110] This method of calculating and predicting the projected area on the solar power station has significant advantages in many aspects. By calculating the proportion of shaded feature pixels, the cloud cover degree of the sky can be intuitively judged, providing an intuitive and important reference index for subsequent analysis. When there are more clouds, the wind speed data is obtained by combining with the wind speed detection device, and then the wind power spillage data is matched according to the wind speed and the real-time power generation power, and the projected area is corrected. This series of operations comprehensively considers the meteorological factors affecting solar power generation. The traditional method of calculating the projected area only relying on image analysis often only considers the static coverage of the clouds, ignoring the influence of dynamic factors such as wind on the movement of the clouds. And this method more accurately evaluates the influence of cloud movement on the sunlight of the solar power station by introducing the wind speed and wind power spillage data, making the calculation of the projected area closer to the actual situation and providing strong support for accurately calculating the solar energy power generation data.

[0111] Through the analysis of the wind power spillage data, the relationship between wind power and power generation power can be deeply understood. During the operation of the microgrid, wind power generation and solar power generation are interrelated and mutually influential. When the wind power spillage data is greater than the reference value, the projected area is corrected, which can timely adjust the estimation of the solar power generation capacity. This provides more comprehensive and accurate information for power dispatching. The dispatching system can reasonably allocate the power generation of each power station based on more accurate solar energy power generation data. For example, when it is estimated that the solar power generation capacity decreases due to cloud cover, the power generation of the biomass power station or the wind power station can be appropriately increased to maintain the power balance of the microgrid, optimize the power dispatching decision, and improve the power quality and power supply stability of the microgrid.

[0112] Taking into account the impact of wind speed and wind force on cloud movement, the power dispatching system can better adapt to complex and changeable meteorological conditions. Under different wind and cloud conditions, the evaluation of solar power generation can be dynamically adjusted. Whether in weather with a light breeze and slow-moving clouds, or in severe weather with howling winds and rapidly changing clouds, the situation of solar power generation can be accurately evaluated, which enhances the ability of microgrids to cope with complex meteorological environments, ensures that various loads can be stably and reliably powered under various weather conditions, improves the reliability and adaptability of microgrid operation, and provides a solid guarantee for ensuring the power demand of social production and life.

[0113] Reference Figure 4 As a core component of the stable power supply of the microgrid, the performance of the energy storage station under different environmental conditions is crucial to the stable operation of the microgrid. The following is a preheating method for the energy storage station, which aims to ensure that it can stably perform its energy storage and power supply regulation functions under complex working conditions.

[0114] First, the system monitors the current temperature and wind overflow data of the energy storage station in real time. When the current temperature is lower than the preset reference temperature (the reference temperature is usually determined based on the type of energy storage station, battery characteristics, and actual operating experience, for example, the performance of some energy storage batteries will drop significantly when the temperature is lower than 5°C, and 5°C can be set as the reference temperature), and the wind overflow data is greater than the preset overflow reference data (the overflow reference data is also based on the operating characteristics and historical data of the wind power station, such as 15% of the rated power of the wind power station), the subsequent preheating adjustment process is triggered. This dual-condition trigger mechanism fully considers the potential impact of ambient temperature and wind power generation conditions on the energy storage station. In a low temperature environment, the chemical reaction rate of the energy storage battery slows down, the internal resistance increases, and the charging and discharging efficiency is reduced; when the wind overflow data is large, it means that the wind power station has a strong power generation capacity. At this time, if the energy storage station performs poorly, it may not be able to effectively store excess electricity, affecting the overall balance of the microgrid.

[0115] Once the triggering conditions are met, the system will match the influencing factors of the energy storage station's power output based on the correspondence between the historical wind overflow data and temperature changes. During the long-term operation, the system has accumulated a large amount of actual data on the power output of the energy storage station under different wind overflow data and temperature conditions. Through data mining and analysis technology, a correlation model between the two was established. For example, when the wind overflow data is in a certain range and the temperature is at a low level, the power output of the energy storage station will show a corresponding downward trend. By querying and matching these historical data, the influencing factors of the energy storage station's power output under the current working conditions can be determined. This factor comprehensively reflects the degree of influence of environmental conditions on the performance of the energy storage station, and provides a key basis for subsequent preheating power adjustment.

[0116] According to the matched power output influencing factors, the system will accurately adjust the preheating power of the energy storage station. There is a clear positive correlation here: the greater the power output influencing factor, the more serious the negative impact of environmental factors on the performance of the energy storage station. At this time, a larger preheating power is required to increase the temperature of the energy storage station and improve its performance. Therefore, the greater the preheating power; conversely, the smaller the power output influencing factor, the smaller the preheating power. In the actual adjustment process, the power output of the heating equipment (such as heating wires, heating plates, etc.) inside the energy storage station is adjusted through an intelligent control system. For example, when the power output influencing factor is large, the system will increase the power supply current or voltage of the heating equipment to make it output higher power and quickly increase the temperature of the energy storage station; when the influencing factor is small, the power of the heating equipment is reduced to save energy.

[0117] Assume that we quantify the factors affecting power output into a numerical range of 1-10, with 1 indicating a very small impact factor and 10 indicating a very large impact factor. The base power of the internal heating equipment of the energy storage station is 5 kilowatts, which can be adjusted to a maximum of 20 kilowatts.

[0118] When the power output influencing factor is 3, it indicates that the environmental factors have little negative impact on the performance of the energy storage station. The intelligent control system adjusts the power of the heating equipment to 8 kilowatts according to the established adjustment strategy through calculation. Because the influencing factor is small, it only needs to increase the power appropriately to meet the needs of the energy storage station to maintain normal performance.

[0119] When the power output factor rises to 8, it means that the environmental factors have a serious negative impact on the performance of the energy storage station. At this time, the intelligent control system will respond quickly, increase the power supply current of the heating equipment, and increase the power of the heating equipment to 15 kilowatts. By outputting higher power, the temperature of the energy storage station is quickly increased to improve its performance in harsh environments.

[0120] This energy storage station preheating method has significant advantages in practical applications. In the case of low temperature and abnormal wind overflow data, the power output influencing factors are matched based on historical data, and then the preheating power of the energy storage station is adjusted. Accurate regulation is performed according to the size of the influencing factors, which can effectively compensate for the negative impact of environmental factors on the performance of the energy storage station. In the cold winter, when the wind is sufficient but the performance of the energy storage station decreases due to low temperature, reasonable preheating adjustment can ensure that the energy storage station can normally store the excess electricity generated by the wind power station and avoid energy waste. At the same time, when power supply is needed, it can also output power stably and continue to exert the functions of energy storage and power supply regulation.

[0121] As a key link in the stable power supply of the microgrid, the energy storage station ensures its normal power output in harsh environments by reasonably adjusting the preheating power. This can effectively reduce the power fluctuations of the microgrid caused by the performance degradation of the energy storage station. For example, in some scenarios with extremely high requirements for power stability (such as data centers, hospitals, etc.), the stable power supply of the energy storage station can avoid equipment failures or medical accidents caused by power fluctuations, greatly improving the stability and reliability of the microgrid's power supply to various loads.

[0122] Adjusting the preheating power according to the magnitude of the factors affecting power output avoids waste or insufficient input of energy. Compared with the traditional fixed-power preheating method, this precise adjustment method can not only meet the requirements for improving the performance of the energy storage station but also maximize the energy utilization efficiency. In today's increasingly high energy costs, it reduces the energy costs of the microgrid operation and improves the economic benefits and sustainable development capabilities of the microgrid.

[0123] The method for the charging priority of the energy storage station includes the following steps:

[0124] Judgment of solar power generation access:

[0125] The system continuously and real-time obtains the solar power generation power of the solar power station. The solar power generation power is accurately measured by the power monitoring equipment installed in the solar power station and transmitted to the central control system of the microgrid in real time through the Internet of Things technology. When the solar power generation power of the solar power station is greater than the set light energy start threshold (this threshold is determined comprehensively based on factors such as the installed capacity of the solar power station, local lighting conditions, and the charging requirements of the energy storage station. For example, in areas with relatively rich light resources, the light energy start threshold can be set at 30% of the rated power of the solar power station to ensure the priority utilization of solar energy under good lighting conditions), it is determined that the solar energy is in an available state. At this time, the central control system issues an instruction to control the switching switch to connect the solar power generation to the charging circuit of the energy storage station. This strategy of preferentially using solar energy is because solar energy, as a clean and renewable energy source, giving priority to its use when conditions are met can minimize the dependence on other energy sources, reduce carbon emissions, and energy costs.

[0126] Judgment of wind power generation access:

[0127] When the solar power generation is lower than the set lower threshold of light energy (this lower threshold is usually lower than the light energy startup threshold, for example, set to 10% of the rated power of the solar power station to ensure timely energy switching when the solar power generation is significantly insufficient), the system will quickly detect the status of wind energy and biomass energy. For wind power stations, their power generation is monitored in real time. When the real-time power generation of the wind power station reaches the set startup value of wind energy (this value is also determined according to factors such as the actual power generation capacity of the wind power station and the charging demand of the energy storage station, such as 20% of the rated power of the wind power station), the central control system will issue another command to control the switching switch to connect the wind energy to the charging circuit of the energy storage station, taking over from solar energy to charge the energy storage station. This process realizes the timely utilization of wind energy resources when solar power generation is insufficient, ensures the uninterrupted charging process of the energy storage station, and also gives full play to the power generation capacity of the wind power station, avoiding energy waste.

[0128] Judgment on the connection of biomass power generation:

[0129] When the real-time power generation of the wind power station is lower than the set lower threshold of wind energy (generally lower than the startup value of wind energy, such as set to 5% of the rated power of the wind power station to ensure considering other energy sources when wind power generation is severely insufficient), the system will further judge the status of biomass energy. By obtaining the mass-energy power generation data of the biomass power station, if the mass-energy power generation data is greater than the set mass-energy startup threshold (this threshold is determined according to factors such as the scale of the biomass power station, fuel supply situation, and the charging demand of the energy storage station, for example, 25% of the rated power of the biomass power station), the central control system will control the switching switch to connect the biomass energy to the charging circuit of the energy storage station, taking over from wind energy to charge the energy storage station. Such sequential control ensures that when both solar energy and wind energy cannot meet the charging demand of the energy storage station, biomass energy can be put into use in a timely manner to maintain the stable charging of the energy storage station.

[0130] This sequential control of charging the energy storage station battery follows the principle of giving priority to solar energy, followed by wind energy and biomass energy, and biomass energy giving priority to powering the load. By setting different startup thresholds and lower thresholds for different energy sources, the orderly utilization of energy is achieved. Solar power generation is preferentially used to charge the energy storage station. When the solar power generation is insufficient, it is sequentially switched to wind energy and biomass energy, ensuring that each energy source can be fully utilized when its power generation meets the conditions, avoiding energy waste, and greatly improving the energy utilization efficiency of the entire microgrid system.

[0131] Switching orderly among solar energy, wind energy and biomass energy makes the charging process of the energy storage station more stable and reliable. As a key link in the microgrid, the stable charging state of the energy storage station helps to maintain the power balance of the microgrid. In the case of fluctuations in different energy generations, such as reduced solar power generation on cloudy days and insufficient wind power generation without wind, it can still provide continuous and stable power supply for the load, enhancing the ability of the microgrid to cope with energy fluctuations.

[0132] Reasonably utilizing multiple energy sources for charging reduces the dependence on a single energy source and lowers the additional energy procurement cost caused by insufficient supply of a certain energy source. For example, in winter with scarce solar energy resources or during continuous cloudy days, wind energy and biomass energy can be supplemented in a timely manner to ensure the normal operation of the microgrid. At the same time, improving energy utilization efficiency also indirectly reduces the operating cost of the microgrid, enhances economic benefits, and makes the microgrid more sustainable both economically and environmentally.

[0133] The embodiment of the present application also discloses a power dispatching system for a microgrid based on the Internet of Things, including a processor, and the processor executes the steps of the power dispatching method for a microgrid based on the Internet of Things as described in any one of the above.

[0134] The embodiment of the present application also discloses a storage medium, in which a program is stored, and when the program is executed by the processor, the steps of the power dispatching method for a microgrid based on the Internet of Things as described in any one of the above are implemented.

[0135] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limitations to the present application. Those of ordinary skill in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present application.

Claims

1. A power dispatching method for a microgrid based on the Internet of Things, characterized in that: The steps include: The camera of the solar power station obtains the regional image in the set area; Extracting light source features and shielding features from the region image; establishing a virtual map according to the regional image based on the coordinates of the solar power station, and marking the light source features and the shielding features from the virtual map; Matching a light source route corresponding to the light source feature from a preset database; Calculating the moving route of the shielding feature within a recent preset time period as the shielding route; In the same time axis, a projection route is obtained by calculating based on the light source characteristics, the light source route, the shielding characteristics and the shielding route; The projected route is the route of the shielding feature projected on the solar power plant, including the passed route and the unpassed route; The route that has been passed is the actual route, and the route that has not been passed is the predicted route; The predicted route is extended from the end according to the change trend of the end of the actual route; Calculating a projection area of ​​the predicted route on the solar power station; Calculate the light energy power generation data of the solar station = 1 - the projected area / the total area of ​​the solar station; Acquire the real-time power generation of the wind power station, and match the wind power generation data of the wind power station from a preset wind power database according to the real-time power generation; Acquire the current temperature and current power of the energy storage station, and match the energy storage power generation data of the energy storage station from a preset energy storage database based on the current temperature; Obtain the mass-energy power generation data of biomass power plants; Calculate comprehensive power generation data based on the solar power generation data, the wind power generation data and the energy storage power generation data; The mass-energy power generation data is adjusted according to the difference between the comprehensive power generation data and the preset power generation reference data. The larger the difference is, the larger the mass-energy power generation data is, and the smaller the difference is, the smaller the mass-energy power generation data is.

2. The power dispatching method of microgrid based on Internet of Things according to claim 1, characterized in that: The method for adjusting the mass-energy power generation data also includes the following steps: Based on the projection route, calculating the predicted distance between the shielding feature and the light source feature within a future set time period; The increment of the mass-energy-power generation data is adjusted according to the anti-correlation of the predicted distance. The larger the predicted distance is, the smaller the increment of the mass-energy-power generation data is; and the smaller the predicted distance is, the larger the increment of the mass-energy-power generation data is.

3. The power dispatching method of microgrid based on Internet of Things according to claim 2 is characterized in that: The method for calculating the projected area of ​​the predicted route on the solar power plant further comprises the following steps: Acquire all solar street light data within a preset range of the solar station, the solar street light data including location information and power information, and use each solar street light data as an element to construct a temporary power detection array, wherein the location information is used as the element position and the power information is used as the element information; Extracting power variation characteristics according to element positions and element information in the temporary power detection array; Calculate the movement trend and deformation trend of the power change feature in the temporary power detection array within a preset time range; Obtaining the light energy influence trend through weighted calculation according to the movement trend and the deformation trend; The projection area is corrected according to the light energy influence trend. The larger the light energy influence trend is, the larger the projection area is, and the smaller the light energy influence trend is, the smaller the projection area is.

4. The power dispatching method of microgrid based on Internet of Things according to claim 3 is characterized in that: The method for calculating the projection area of ​​the predicted route on the solar power plant further includes the following sub-steps: According to the vector corresponding to the moving trend Correction of the actual route for , The corrected formula A is: ;in, is the adjustment factor; According to the deformation trend Correct the shortest distance between the predicted route and the solar station for , The corrected formula B is: .

5. The power dispatching method of microgrid based on Internet of Things according to claim 1, characterized in that: The method for calculating the projected area of ​​the predicted route on the solar power plant further comprises the following steps: Calculating the pixel ratio of the shielding feature in the regional image, and if the pixel ratio is greater than a set value, acquiring real-time wind speed data based on a preset wind speed detection device; Matching wind power overflow data according to the wind speed data and the real-time power generation power; If the wind overflow data is greater than the preset overflow reference data, the projection area is corrected according to the wind overflow data. The larger the wind overflow data is, the larger the projection area is, and the smaller the wind overflow data is, the smaller the projection area is.

6. The power dispatching method of microgrid based on Internet of Things according to claim 5 is characterized in that: The preheating method of the energy storage station comprises the following steps: When the current temperature is lower than the preset reference temperature, and the wind overflow data is greater than the preset overflow reference data; Based on the corresponding relationship between the historical wind overflow data and temperature changes, the power output influencing factors of the energy storage station are matched; The preheating power of the energy storage station is adjusted according to the power output influencing factor. The greater the power output influencing factor is, the greater the preheating power is; and the smaller the power output influencing factor is, the smaller the preheating power is.

7. The power dispatching method of microgrid based on Internet of Things according to claim 1, characterized in that: The method for charging priority of energy storage stations comprises the following steps: The solar power generation power of the solar station is obtained. When the solar power generation power of the solar station is greater than a set light energy start threshold, it is determined that the solar energy is available, and a switching switch is controlled to connect the solar power generation to the charging circuit of the energy storage station; If the solar power generation power is lower than the set light energy lower limit threshold, the status of wind energy and biomass energy is detected; if the real-time power generation power of the wind power station is set to the wind energy start-up constant value, the switch is controlled to connect the wind energy to the charging circuit of the energy storage station, replacing the solar energy to charge the energy storage station; When the real-time power generation of the wind power station is lower than the set wind energy lower limit threshold, the biomass energy status is judged; if the biomass energy power generation data is greater than the set biomass energy start threshold, the control switching switch will connect the biomass energy to the charging circuit of the energy storage station, replacing wind energy to charge the energy storage station.

8. A power dispatching system for microgrid based on the Internet of Things, characterized in that: It includes a processor, in which the steps of the power dispatching method of a microgrid based on the Internet of Things are executed as described in any one of claims 1 to 7.

9. A storage medium, characterized in that: The medium stores a program, and when the program is executed by the processor, the steps of the power dispatching method for a microgrid based on the Internet of Things described in any one of claims 1 to 7 are implemented.

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