Power dispatching method and system of microgrid based on Internet of Things, and storage medium

Abstract

The invention relates to the technical field of micro-grids, and discloses a micro-grid power dispatching method and system based on the Internet of Things and a storage medium, and the method comprises the steps: obtaining a regional image through a solar station camera, extracting light source and shielding features, building a virtual map, marking related features, matching a light source route, and calculating a shielding route; a projection route and a projection area are obtained, and solar energy power generation data of the solar station are calculated. Acquiring real-time power generation power of the wind power station, current temperature and electric quantity of the energy storage station and mass energy power generation data of the biomass power station, and calculating comprehensive power generation data; and finally, adjusting the mass energy power generation data according to a difference value between the comprehensive power generation data and preset power generation reference data. The method can improve the power quality of the micro-grid 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 be operated in parallel with an external power grid or in isolation.

[0003] Microgrids include distributed power sources, energy storage devices, energy conversion devices, loads, and monitoring and protection devices. Distributed power sources include solar photovoltaic, 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 microgrids. Energy storage devices include batteries, supercapacitors, flywheel energy storage, etc. Energy storage devices can store energy when there is excess power and release energy when there is insufficient power, which plays a role in smoothing power fluctuations and improving power supply reliability and stability. Energy conversion devices mainly include power electronic converters, etc., which are used to realize the conversion between different voltage levels and different frequencies of electrical energy, as well as the control of distributed power sources and energy storage devices so that they can effectively cooperate with other parts of the microgrid. Loads cover various types of electrical equipment, such as residential electricity, commercial electricity, industrial electricity, etc. According to their importance, loads can be divided into critical loads and non-critical loads, and microgrids will give priority to ensuring the power supply of critical loads. Monitoring and protection devices are used to monitor the operating status of microgrids in real time, including parameters such as voltage, current, frequency, and power, 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 when a fault occurs to ensure the safety of microgrid equipment and personnel.

[0004] However, the existing microgrid is dispatched according to the power fluctuations of different distributed power sources. During the dispatch, the power has already fluctuated significantly and the power quality has deteriorated to a great extent. Therefore, a method is needed to dispatch power in a timely manner and alleviate the deterioration of power quality. Summary of the invention

[0005] In order to improve the power quality of a 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, which adopts the following technical solution:

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

[0008] The camera of the solar power station obtains the regional image in the set area;

[0009] Extracting light source features and shielding features from the region image;

[0010] 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;

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

[0012] Calculating the moving route of the shielding feature within a recent preset time period as the shielding route;

[0013] In the same time axis, a projection route is calculated based on the light source feature, the light source route, the shielding feature and the shielding route; the projection route is the route projected by the shielding feature on the solar power station, including a passed route and a non-passed route; the passed route is the actual route, and the non-passed route is the predicted route; the predicted route is extended from the end according to the change trend of the end of the actual route;

[0014] Calculating a projection area of ​​the predicted route on the solar power station;

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

[0016] 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;

[0017] 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;

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

[0019] Calculate comprehensive power generation data based on the solar power generation data, the wind 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 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.

[0021] By adopting the above technical solution, by calculating the data of each power station in advance and adjusting the quality and energy power generation data according to the difference between the comprehensive power generation data and the preset reference data, the dispatch adjustment can be made before the power fluctuation, effectively alleviating the decline in power quality. Compared with the traditional dispatching method after the power fluctuates greatly, the power quality of the microgrid during the dispatching process can be significantly improved. By using the solar station camera to obtain images and analyze them, the impact of shielding features (clouds) on solar power generation can be accurately predicted, and power dispatch can be planned in advance. At the same time, combined with the data of wind power stations, energy storage stations and biomass power stations, multi-power coordinated optimization dispatching can be achieved to improve the reliability and stability of microgrid power supply. Comprehensively consider the power generation capacity of various distributed power sources, flexibly adjust the power generation strategy according to the real-time situation of different energy sources, so that various types of energy can be fully utilized in the microgrid, improve energy utilization efficiency, and reduce energy waste.

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

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

[0024] 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.

[0025] By adopting the above technical solution, the increment of mass-energy power generation data is adjusted by calculating the predicted distance between the shielding characteristics and the light source characteristics in the future set time period. On the basis of the original adjustment based on the distance between the shielding characteristics (clouds) and the light source characteristics (sun), the predictive adjustment of future situations is added. It can not only cope with the degree of impact of solar energy at present, but also make arrangements in advance, so that power dispatching is more forward-looking in the time dimension, more accurately matches the real-time power demand of the microgrid, and continuously improves the quality of power. According to the anti-correlation adjustment of the predicted distance, the increment of mass-energy power generation data is adjusted. When it is predicted that the degree of impact of solar energy is small, the increment of biomass power generation is reduced in advance to avoid excessive production and waste of energy; when it is predicted that the impact of solar energy is large, the increment of biomass power generation is increased in advance to fill the future power gap in time, so that the energy utilization of the microgrid is more reasonable in the time span, and the energy cost is further reduced. Through the early adjustment strategy for future situations, the microgrid can prepare for power supply adjustment in advance when facing potential fluctuations in solar power generation, which greatly reduces the problems of 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 microgrid operation are guaranteed, a more stable and reliable power supply is provided for various loads, and the power demand of critical and non-critical loads at different times is met.

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

[0027] 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;

[0028] Extracting power variation characteristics according to element positions and element information in the temporary power detection array;

[0029] Calculate the movement trend and deformation trend of the power change feature in the temporary power detection array within a preset time range;

[0030] Obtaining the light energy influence trend through weighted calculation according to the movement trend and the deformation trend;

[0031] 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.

[0032] By adopting the above technical solution, a temporary power detection array is constructed by acquiring solar street light data, extracting power change characteristics from the array and analyzing its movement trend and deformation trend, and then obtaining the light energy impact trend to correct the projection area. This method comprehensively considers the actual light changes around the solar station. Compared with relying solely on camera image analysis, it can more comprehensively and accurately reflect the degree of solar energy impact, making the calculation of the projection area more realistic, and laying the foundation for the subsequent accurate calculation of light power generation data. Using solar street light data, the light conditions in a small area around the solar station are taken into consideration, and are no longer limited to the analysis of sky area images obtained only from the solar station camera. This makes the evaluation of solar power generation more comprehensive, taking into account 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, and improve the comprehensiveness and reliability of microgrid power dispatching. Real-time analysis of the movement trend and deformation trend of the power change characteristics of solar street lights can capture the dynamic changes of the light environment in a timely manner. The projection area is corrected according to the light energy influence trend obtained from these dynamic changes, so that the power dispatching system can adaptively adjust according to the real-time changes of the actual lighting environment, enhancing the flexibility and adaptability of microgrid power dispatching under different lighting conditions and ensuring the stable operation of the microgrid.

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

[0034] According to the vector corresponding to the moving trend Correction of the actual route for ,

[0035] The corrected formula A is:

[0036] ;in, is the adjustment factor;

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

[0038] The corrected formula B is:

[0039] .

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

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

[0042] 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;

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

[0044] 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.

[0045] By adopting the above technical solution, the proportion of obscured feature pixels is calculated, and the degree of cloud coverage in the sky can be intuitively judged. When there are many clouds, wind speed data is obtained in combination with the wind speed detection device, and then the wind overflow data is matched according to the wind speed and the real-time power generation to correct the projection area. This series of operations comprehensively considers the meteorological factors affecting solar power generation. Compared with relying solely on image analysis, it more accurately evaluates the impact of cloud movement on the illumination of the solar station, making the projection area calculation closer to reality, and providing strong support for the accurate calculation of solar power generation data. Through the analysis of wind overflow data, the relationship between wind power and power generation is deeply understood. When the wind overflow data is greater than the reference value, the projection area is corrected, and the estimation of solar power generation capacity can be adjusted in time. 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 power generation data, optimize power dispatching decisions, and improve the power quality and power supply stability of the microgrid. Considering 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 assessment of solar power generation can be dynamically adjusted, which enhances the ability of the microgrid to cope with complex meteorological environments, ensures stable and reliable power supply for various loads under all weather conditions, and improves the reliability and adaptability of microgrid operation.

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

[0047] When the current temperature is lower than the preset reference temperature, and the wind overflow data is greater than the preset overflow reference data;

[0048] 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;

[0049] 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.

[0050] By adopting the above technical solution, 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 based on the size 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 continue to play the energy storage and power supply regulation functions. The energy storage station is a key link in the stable power supply of the microgrid. By reasonably adjusting the preheating power to ensure that the energy storage station can output power normally in harsh environments, the power fluctuations caused by the performance degradation of the energy storage station in the microgrid can be effectively reduced, and the stability and reliability of the microgrid's power supply to various loads can be greatly improved. The preheating power is adjusted according to the size of the power output influencing factors to avoid energy waste or insufficient investment. It can not only meet the performance improvement needs of the energy storage station, but also maximize the energy utilization efficiency and reduce the energy cost of the microgrid operation.

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

[0052] 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;

[0053] 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;

[0054] 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.

[0055] By adopting the above technical solution, the battery charging sequence is controlled, with solar energy being the priority, followed by wind energy and biomass energy, and biomass energy being the priority to supply power to the load. By setting the start-up threshold and lower limit threshold of different energy sources, solar power generation is used to charge the energy storage station first. When the solar power generation power is insufficient, it is switched to wind energy and biomass energy in turn to ensure that each energy source can be fully utilized when its power generation power meets the conditions, avoiding energy waste and improving the energy utilization efficiency of the entire microgrid system. Orderly switching between 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 energy storage station's stable charging state helps maintain the power balance of the microgrid. In the case of fluctuations in power generation of different energy sources, it can still provide continuous and stable power supply to the load, enhancing the microgrid's ability to cope with energy fluctuations. The rational use of multiple energy sources for charging reduces 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 the second aspect, the present application provides a power dispatching system for a microgrid based on the Internet of Things, which adopts the following technical solutions:

[0057] A power dispatching system for a microgrid based on the Internet of Things comprises a processor, wherein 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.

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

[0059] A storage medium stores a program, and when the program is executed by a processor, the steps of the power dispatching method of a microgrid based on the Internet of Things are implemented as described in any one of the above.

[0060] In summary, the present application includes at least one of the following beneficial technical effects: The present microgrid power dispatching method based on the Internet of Things first obtains images through the solar station camera, analyzes and calculates the light power generation data, combines the data of wind power stations, energy storage stations and biomass power stations to calculate the comprehensive power generation data, and then adjusts the mass-energy power generation data according to the difference between the data and the preset value. At the same time, it optimizes from many aspects: adjusting the mass-energy power generation increment according to the predicted distance of the projection route; correcting the projection area using solar street light data; using formulas to correct the distance between the actual route and the predicted route; correcting the projection area by considering wind speed and cloud pixel ratio; adjusting the preheating power of the energy storage station according to environmental factors; and controlling the charging of the energy storage station according to energy priority. These measures can accurately predict and dispatch in advance, improve the quality of electricity and the reliability of power supply, improve energy utilization efficiency, reduce costs, and enhance the ability of microgrids to cope with complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 It is a step diagram of a power dispatching method of a microgrid based on the Internet of Things.

[0062] Figure 2 The method for calculating the projected area of ​​the predicted route on the solar power plant includes steps.

[0063] Figure 3 The method for calculating the projection area of ​​the predicted route on the solar power plant also includes a step diagram.

[0064] Figure 4 It is a step diagram of the preheating method of the energy storage station. DETAILED DESCRIPTION

[0065] Embodiments of the present application are described in detail below, examples of which are shown in the accompanying drawings.

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

[0067] The present application embodiment discloses a power dispatching method for a microgrid based on the Internet of Things, referring to Figure 1 , including the following steps:

[0068] Deploy high-definition cameras at solar power stations and use IoT technology to obtain regional images in the set area in real time. 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 shielding features (such as clouds, etc.) are accurately extracted from regional images. These features are key factors in 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 shielding features are clearly marked to achieve a visual presentation of the lighting environment around the solar power station. At the same time, the matching algorithm is used to quickly match the light source route corresponding to the light source feature from the preset database, which stores a large amount of sun trajectory data at different times, seasons, and geographical locations. In the recent preset time period (for example, 5-10 minutes), the motion analysis algorithm is used to calculate the movement route of the shielding feature, which is defined as the shielding route, to track the movement trajectory of shielding objects such as clouds.

[0070] In the same timeline, based on the light source characteristics, light source routes, shielding characteristics and shielding routes, a complex geometric calculation model is used to calculate the projection route. This projection route is the route of the shielding feature projected on the solar power station. The route that has passed is the actual route, which is determined by the actual monitoring data of the shielding projection in the past time period; the route that has not passed is the predicted route, which is extended from the end using the trend extrapolation algorithm based on the change trend at the end of the actual route. After that, the projection 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 impact on solar power generation.

[0071] According to the calculated projection area, the solar power generation data of the solar station is calculated by the formula "solar power generation data of the solar station = 1-projection area / total area of ​​the solar station", which directly reflects the real-time status of solar power generation. At the same time, the real-time power generation of the wind power station is obtained in real time through the IoT sensor. According to this power data, the wind power generation data of the wind power station is matched from the preset wind power database (which stores the power generation data under different wind speeds, wind directions, etc.). The current temperature and current power of the energy storage station are obtained, and the energy storage characteristic model is used to match the energy storage power generation data of the energy storage station from the 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, based on the solar power generation data, wind power generation data and energy storage power generation data, the weighted comprehensive calculation model is used to calculate the comprehensive power generation data to comprehensively evaluate the current power generation capacity of the microgrid.

[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 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 microgrid power generation is achieved.

[0073] Assume that at a certain moment, the preset reference power generation data is 1000 kilowatts, and the calculated comprehensive power generation data is 800 kilowatts, and the difference is 200 kilowatts. Due to the large difference, according to the rule of "the larger the difference, the larger the mass-energy power generation data", the biomass power station needs to increase its power generation. For example, the original mass-energy power generation data of the biomass power station is 100 kilowatts, and now it is adjusted to 300 kilowatts to make up for the gap between the comprehensive power generation data and the reference data, so that the overall power generation situation is closer to the preset value.

[0074] For example, at another moment, the preset power generation reference data is still 1000 kilowatts, while the comprehensive power generation data is calculated to be 950 kilowatts, with a difference of 50 kilowatts. Because the difference is relatively small, according to the rules, the biomass power station does not need to significantly adjust the power generation; for example, if the original mass-energy power generation data is 150 kilowatts, it is now appropriately adjusted to 170 kilowatts. Through fine-tuning, the comprehensive power generation data is more in line with the preset power generation reference data, thereby achieving precise control of microgrid power generation and ensuring the stable operation of the microgrid and the balance of power supply.

[0075] By calculating the data of each power station in advance and adjusting the quality and 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 the power fluctuates, effectively alleviating the decline in power quality. Compared with the traditional method of scheduling after the power fluctuates significantly, it can significantly improve the power quality of the microgrid during the scheduling process and reduce equipment damage and production interruptions caused by power quality problems. Using the solar station camera to obtain images and analyze them, it can accurately predict the impact of shielding features (clouds) on solar power generation, plan power scheduling in advance, and combine the data of wind power stations, energy storage stations and biomass power stations to achieve multi-power coordinated optimization scheduling, improve the reliability and stability of microgrid power supply, and ensure the continuous and stable power consumption of various loads. Comprehensively consider the power generation capacity of various distributed power sources, flexibly adjust the power generation strategy according to the real-time situation of different energy sources, so that various energy sources can be fully utilized in the microgrid, improve energy utilization efficiency, reduce energy waste, reduce dependence on a single energy source, and improve the economic and environmental benefits of the microgrid.

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

[0077] First, based on the previously calculated projection route, advanced trajectory prediction algorithms and time series analysis techniques are used to calculate the predicted distance between the shielding features (mainly clouds) and the light source features (the sun) within a set time period in the future (for example, the next 1-2 hours). During the calculation process, the moving speed and direction changes of the clouds and the trajectory of the sun in the sky are fully considered. These data are obtained through high-precision sensors and real-time monitoring systems. For example, the cloud dynamics data provided by meteorological satellites and the sun position monitoring equipment deployed at solar power stations are used to ensure the accuracy of the predicted data.

[0078] Then, according to the predicted distance, the increment of mass-energy power generation data is adjusted in an anti-correlation manner, and a mathematical model is established to quantify the relationship between the predicted distance and the increment of mass-energy power generation data. Specifically, the larger the predicted distance, the smaller the impact of clouds on solar power generation in the future time period, and the smaller the increment of mass-energy power generation data; the smaller the predicted distance, the greater the possibility of cloud cover on solar power generation, and the larger the increment of mass-energy power generation data. Through this anti-correlation adjustment mechanism, precise control of the power generation of biomass power stations can be achieved.

[0079] The mathematical model established is assumed to be: mass-energy power generation data increment = 1000 / prediction distance (unit: km), where 1000 is a coefficient determined comprehensively based on the power generation characteristics of the microgrid, historical data, and power generation demand.

[0080] If the predicted distance is 5 kilometers, 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 its power generation by 200 kilowatts to cope with the possible reduction in solar power generation.

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

[0082] Assume that the predicted distance is reduced to 2 kilometers, and the increment of mass-energy power generation data is 1000 / 2=500 kilowatts. As the predicted distance becomes smaller, the possibility of cloud cover blocking solar power generation increases, so the increment of mass-energy power generation data increases significantly, and the biomass power station needs to increase its power generation by 500 kilowatts to ensure the stability of the power supply of the microgrid.

[0083] On the basis of the original real-time distance adjustment based on the shielding characteristics (clouds) and light source characteristics (sun), predictive adjustment for future situations has been added. In the past, scheduling was only based on the current distance between the cloud layer and the sun, which often had lags and was difficult to cope with rapidly changing weather conditions. Now, by predicting the future distance, we can not only cope with the degree of impact on the current solar energy, but also make arrangements in advance, making power scheduling more forward-looking in the time dimension. For example, when it is predicted that the clouds will gradually move away from the sun in the future and solar power generation will be less affected, the increment of biomass power generation can be reduced in advance, the overproduction and waste of energy can be avoided, and the excess biomass raw materials can be stored for use when they are more needed later. On the contrary, when it is predicted that the solar energy is greatly affected, the increment of biomass power generation is increased in advance to fill the future power gap in time and ensure the stability of the power supply of the microgrid. This makes the energy utilization of the microgrid more reasonable in terms of time span, further reduces energy costs, and improves the economic benefits of the entire microgrid system.

[0084] From the perspective of microgrid operation stability, through the early adjustment strategy for future situations, the microgrid can prepare for power supply adjustment in advance when facing potential fluctuations in solar power generation. When possible cloud cover is detected, biomass power generation is increased in advance to avoid sudden changes in power supply caused by a sudden drop in solar power generation, greatly reducing the problems of large fluctuations in voltage and frequency caused by sudden changes in power supply. In a complex and changeable power generation environment, whether it is sudden cloud cover on a sunny day or fluctuations in solar power generation caused by gradual changes in weather, the stability and reliability of microgrid operation can be guaranteed. This provides a more stable and reliable power supply for various loads, meets the power demand of key loads (such as hospitals, financial institutions and other important places that cannot be powered off) and non-key loads (such as ordinary residents' electricity, some commercial electricity, etc.) at different times, and ensures the normal operation of the entire social production and life.

[0085] Reference Figure 2 In the process of accurately evaluating solar power generation and optimizing microgrid power dispatch, calculating the projection area of ​​the predicted route on the solar power station is an extremely critical link. By introducing solar street light data, the optimization method for calculating the projection area of ​​the predicted route on the solar power station includes the following steps:

[0086] With the help of IoT technology, all solar street light data within the preset range of the solar station (such as a 5-10 km radius around the solar station) are obtained. These solar street light data cover location information (determined by GPS positioning or high-precision geographic information system) and power information (collected in real time by the power sensor built into the street light). Each solar street light data is used as an element to build a temporary power detection array. In this array, the location information clarifies the spatial position of the element, and the power information is used as the key attribute information of the element. In this way, the solar street light data around the solar station is integrated and structured, providing an orderly data basis for subsequent analysis.

[0087] Using data analysis algorithms, we can deeply explore the element positions and element information in the temporary power detection array and extract the power change characteristics. For example, we can analyze the rate of change of the power of solar street lights at different locations over time, as well as the difference in power between adjacent street lights. These characteristics can intuitively reflect the changes in light intensity in different areas and the spatial distribution characteristics of light changes. These characteristics are an important basis for judging the impact of solar energy, and can reveal the dynamic changes of the lighting environment better than simple power values.

[0088] Within a preset time range (e.g., every 15-30 minutes as an analysis period), the movement trend and deformation trend of the extracted power change features in the temporary power detection array are calculated. The movement trend mainly focuses on the movement direction and speed of the power change features in space, for example, whether the light intensity reduction feature in a certain area is approaching the solar power station, and how fast it moves. The deformation trend focuses on analyzing the morphological changes in the power change features, such as whether the range of light intensity changes is expanding or shrinking, whether the gradient of the change has changed, etc. Through the precise calculation of these trends, we can have a more comprehensive understanding of the dynamic evolution of the lighting environment.

[0089] According to the movement trend and deformation trend, the light energy impact trend is obtained by using a weighted calculation model. In this model, different weights are assigned to the movement trend and deformation trend, and the weights are determined based on a large amount of historical data and analysis of the actual lighting environment. For example, in areas where clouds move frequently, the weight of the movement trend may be relatively high; while in scenes with complex lighting changes and diverse morphological changes, the weight of the deformation trend will be appropriately increased. Through reasonable weighted calculation, a light energy impact trend value that can comprehensively reflect the degree of impact of changes in the lighting environment on solar power generation is obtained.

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

[0091] Assuming that the initial calculated projection area is 100 square meters, the light energy impact trend ranges from 0 to 10. The larger the value, the greater the negative impact of the lighting environment on solar power generation. When the light energy impact trend is 2, it indicates that the lighting environment has little impact on solar power generation. According to the correction rule, the projection area needs to be reduced accordingly. Assuming that according to the proportional relationship, the projection area is adjusted to 100×(1-0.2)=80 square meters. This is because a lower light energy impact trend means that obstructions such as clouds have weak interference with light, and the actual area of ​​the solar station blocked is also small.

[0092] When the light energy impact trend increases to 8, it means that the lighting environment has a great negative impact on solar power generation, and the projection area should be increased. According to the correction rule, the projection area becomes 100×(1+0.8)=180 square meters. Because a higher light energy impact trend means more obstructions such as clouds, which block a wider range of solar power stations and have a greater impact on solar power generation, the projection area is increased to be more in line with the actual lighting conditions.

[0093] Compared with the traditional method of calculating the projection area by relying solely on camera image analysis, this method has significant advantages. By acquiring solar street light data to build a temporary power detection array, and deeply analyzing the power change characteristics and trends, the light energy impact trend is obtained to correct the projection area, and the actual light changes around the solar power station are comprehensively considered. As a device that directly receives light and generates electricity, the power change of solar street lights directly reflects the change in light intensity. This method can more comprehensively and accurately reflect the degree of solar energy impact than relying solely on camera image analysis. This makes the calculation of the projection area more realistic, laying a solid foundation for the subsequent accurate calculation of light power generation data.

[0094] This method takes into account the lighting conditions in a small area around the solar station, and is no longer limited to the analysis of sky area images obtained only from the solar station camera. The solar station camera mainly focuses on the situation of obstructions such as clouds in the sky, but it is difficult to fully capture the lighting differences in local areas on the ground. Solar street lights are distributed in the surrounding area and can sense the changes in lighting at different locations, which makes the evaluation of solar power generation more comprehensive. Taking into account the impact of local area lighting differences on power generation, the status of solar power generation in the entire microgrid can be more accurately grasped. When dispatching electricity, based on a more accurate assessment of the status of solar power generation, more reasonable decisions can be made, which improves the comprehensiveness and reliability of microgrid power dispatch.

[0095] Real-time analysis of the movement and deformation trends of the power change characteristics of solar street lights can capture the dynamic changes of the lighting environment in a timely manner. Whether it is due to cloud movement, building shading or other factors, the changes in lighting can be quickly detected. The projection area is corrected according to the light energy impact trend obtained from these dynamic changes, so that the power dispatching system can adaptively adjust according to the real-time changes in the actual lighting environment. Under different lighting conditions, such as early morning, evening, cloudy weather, etc., the evaluation of solar power generation and the power dispatching strategy can be adjusted in time, which enhances the flexibility and adaptability of microgrid power dispatching under different lighting conditions and ensures the stable operation of microgrids.

[0096] Corrections to actual route based on movement trends:

[0097] According to the vector corresponding to the moving trend To correct the actual route. The movement trend here 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 static and may change due to various factors such as airflow and weather systems. Vector This displacement change in space is accurately captured.

[0098] The specific correction formula A is: .in, represents the coordinates of a point on the actual route initially calculated, and are the corrected coordinates. It is a carefully set adjustment coefficient. Its value is not fixed, but is dynamically adjusted based on a large amount of historical data and real-time meteorological conditions. For example, in weather conditions with strong winds, the movement speed of clouds may increase. The value of will be adjusted accordingly to more accurately reflect the actual movement of the clouds. This quantitative correction method can effectively avoid the problem of inaccurate actual route caused by initial calculation deviation. 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 impact path of clouds and other factors on solar power generation. This is like in a navigation system, adjusting the route planning in real time according to the actual driving conditions of the vehicle, ensuring the subsequent accurate calculation of the projected route and area, and providing reliable basic data for power dispatching.

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

[0100] According to the deformation trend Correct the shortest distance between the predicted route and the solar station. The shape of the shielding feature does not remain fixed during movement, 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 station, which in turn will have different degrees of impact on solar power generation. For example, when the cloud layer gradually stretches during movement, its coverage may expand, and the shortest distance to the solar station will also change accordingly, and the area and degree of solar energy blocking will also be different.

[0101] The corrected formula B is: .in, is the shortest distance between the initially calculated predicted route and the solar station, is the corrected shortest distance. It is also a coefficient that is dynamically adjusted according to the actual situation. It reflects the degree of deformation of the shielding feature. Through the analysis and research of a large number of cloud deformation cases, combined with real-time meteorological monitoring data, it is possible to accurately determine ). The deformation trend is combined with formula B for correction, which fully considers the potential difference in the impact of the shape change of the shading feature during the movement on solar power generation. Compared with the traditional calculation method, this correction method can more accurately predict the extent to which solar power generation will be affected in the future. In power dispatching, it is possible to make adjustments to the power generation plan in advance based on more accurate predictions, reasonably arrange the power generation of other power sources such as biomass power stations and wind power stations, ensure the stable and reliable power supply of the microgrid, and provide continuous and high-quality power services for various loads.

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

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

[0104] When the calculated pixel ratio is greater than the set value (the set value can be adjusted according to the actual situation and historical data, such as the common setting of 30%-40%), the process of obtaining real-time wind speed data based on the preset wind speed detection device is started. The wind speed detection device usually uses a high-precision wind speed sensor, which is installed in a suitable location around the solar station to ensure that the local wind speed information can be accurately captured. These wind speed sensors are connected to the data processing system through the 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, the wind overflow data is matched based on the acquired 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, combined with the real-time power generation of the wind power station, the wind overflow data is calculated. For example, when the wind speed is high, but the power generation of the wind power station does not reach the expected theoretical value, it means that a certain amount of wind power is not fully utilized. The power value corresponding to this part of the wind power that is not effectively converted into electrical energy is the wind overflow data.

[0106] If the calculated wind overflow data is greater than the preset overflow reference data (the reference data can also be set according to the actual situation and historical operation data, such as the common setting of 10%-20% of the rated power of the wind power station), the projected area is corrected according to the wind overflow data. A clear correspondence is established here: the larger the wind overflow data, the higher the degree of underutilization of wind power, which also means that clouds may have a greater shielding effect on the solar power station under the action of wind power, so the projected area is larger; conversely, the smaller the wind overflow data, the smaller the projected area.

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

[0108] When the wind overflow data is large: The wind overflow data obtained in a certain calculation is 250 kilowatts, which is greater than the overflow reference data of 150 kilowatts. Since the wind overflow data is large, it indicates that the wind power is not fully utilized to a high degree, and the clouds may be more likely to block the solar station under the action of strong winds. Assuming that the original preliminary calculated projection area is 200 square meters, according to the corresponding relationship, the projection area corrected according to the wind overflow data is increased to 300 square meters to reflect the greater blocking effect of clouds on the solar station under the influence of wind.

[0109] Wind overflow data hours: Another calculation results in a wind overflow data of 80 kilowatts, which is less than the overflow reference data of 150 kilowatts. At this time, the wind power is not fully utilized to a low degree, and the cloud layer has little effect on the solar station under the action of wind. If the original preliminary calculated projection area is also 200 square meters, the correction reduces the projection area to 150 square meters, which is in line with the inverse correlation between wind overflow data and projection area.

[0110] This method of calculating the projected area of ​​the predicted route on the solar power station has many significant advantages. By calculating the proportion of obscured feature pixels, the degree of cloud coverage in the sky can be intuitively judged, providing an intuitive and important reference indicator for subsequent analysis. When there are many clouds, the wind speed detection device is used to obtain wind speed data, and then the wind overflow data is matched according to the wind speed and real-time power generation to correct the projected area. This series of operations comprehensively considers the meteorological factors that affect solar power generation. The traditional method of calculating the projected area by relying solely on image analysis can often only take into account the static coverage of clouds, while ignoring the impact of dynamic factors such as wind on cloud movement. This method introduces wind speed and wind overflow data to more accurately evaluate the impact of cloud movement on the illumination of the solar station, making the projected area calculation closer to reality and providing strong support for the accurate calculation of solar power generation data.

[0111] By analyzing the wind overflow data, we can gain a deeper understanding of the relationship between wind power and power generation. In the operation of the microgrid, wind power generation and solar power generation are interrelated and influence each other. When the wind overflow data is greater than the reference value, the projected area is corrected and the estimate of solar power generation capacity can be adjusted in time. 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 power generation data. For example, when it is estimated that the solar power generation capacity will decrease due to cloud cover, the power generation of the biomass power station or wind power station can be appropriately increased to maintain the power balance of the microgrid, optimize power dispatching decisions, 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 microgrids, energy storage stations ensure normal power output in harsh environments by reasonably adjusting the preheating power. This can effectively reduce power fluctuations in microgrids caused by the performance degradation of energy storage stations. For example, in some scenarios with extremely high requirements for power stability (such as data centers, hospitals, etc.), stable power supply from energy storage stations can avoid equipment failures or medical accidents caused by power fluctuations, greatly improving the stability and reliability of microgrid power supply to various loads.

[0122] The preheating power is adjusted according to the size of the factors affecting power output, avoiding energy waste or insufficient investment. Compared with the traditional fixed power preheating method, this precise adjustment method can not only meet the performance improvement needs of the energy storage station, but also maximize the energy utilization efficiency. With the increasing cost of energy, it reduces the energy cost of microgrid operation and improves the economic benefits and sustainable development capabilities of microgrids.

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

[0124] Solar power generation access judgment:

[0125] The system will continuously obtain the solar power generation power of the solar station in real time. The solar power generation power is accurately measured by the power monitoring equipment installed in the solar 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 station is greater than the set light energy start threshold (the threshold is determined based on the installed capacity of the solar station, local light conditions, and the charging needs of the energy storage station. For example, in areas with abundant light resources, the light energy start threshold can be set to 30% of the rated power of the solar station to ensure that solar energy is used first when the light conditions are good), the solar energy is determined to be available. At this time, the central control system will issue an instruction to control the switch to connect the solar power generation to the charging circuit of the energy storage station. This strategy of giving priority to the use of solar energy is because solar energy is a clean and renewable energy source. When conditions are met, giving priority to the use of solar energy can minimize dependence on other energy sources and reduce carbon emissions and energy costs.

[0126] Wind power generation access judgment:

[0127] If the solar power generation power is lower than the set light energy lower limit threshold (the lower limit threshold is usually lower than the light energy start threshold, for example, it is set to 10% of the rated power of the solar station to ensure timely switching of energy when the solar power generation power is obviously insufficient), the system will quickly detect the status of wind energy and biomass energy. For wind power stations, their power generation power will be monitored in real time. When the real-time power generation power of the wind power station reaches the set wind energy start-up value (the value is also determined based on the actual power generation capacity of the wind power station, the charging demand of the energy storage station and other factors, such as 20% of the rated power of the wind power station), the central control system will issue an instruction again to control the switching switch to connect wind energy to the charging circuit of the energy storage station, replacing solar energy to charge the energy storage station. This process realizes the timely use of wind energy resources when solar power generation is insufficient, ensuring that the charging process of the energy storage station is not interrupted, while also giving full play to the power generation capacity of the wind power station and avoiding energy waste.

[0128] Judgment on biomass power generation access:

[0129] When the real-time power generation of the wind power station is lower than the set wind energy lower limit threshold (generally lower than the wind energy startup value, such as 5% of the rated power of the wind power station, to ensure that other energy sources are considered when wind power generation is seriously insufficient), the system will further judge the biomass energy status. 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 (the threshold is determined according to the scale of the biomass power station, the fuel supply situation and the charging demand of the energy storage station, such as 25% of the rated power of the biomass power station), the central control system will control the switch to connect the biomass energy to the charging circuit of the energy storage station, replacing the wind energy to charge the energy storage station. Such sequential control ensures that when neither solar energy nor wind energy can meet the charging needs of the energy storage station, biomass energy can be put into use in time 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 is given priority to supplying power to the load. By setting the start threshold and lower limit threshold of different energy sources, orderly utilization of energy is achieved. Solar power generation is used first to charge the energy storage station. When the solar power generation power is insufficient, it is switched to wind power and biomass energy in turn, ensuring that each energy source can be fully utilized when its power generation power meets the conditions, avoiding energy waste and greatly improving the energy utilization efficiency of the entire microgrid system.

[0131] Orderly switching between 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 maintain the power balance of the microgrid. In the case of fluctuations in power generation from different energy sources, such as reduced solar power generation on cloudy days and insufficient wind power generation when there is no wind, it can still provide continuous and stable power supply to the load, enhancing the ability of the microgrid to cope with energy fluctuations.

[0132] The rational use of multiple energy sources for charging reduces the dependence on a single energy source and reduces the additional energy procurement costs caused by insufficient supply of a certain energy source. For example, in winter when solar energy resources are scarce or on consecutive cloudy days, wind energy and biomass energy can be supplemented in time to ensure the normal operation of the microgrid. At the same time, improving energy utilization efficiency also indirectly reduces the operating costs of the microgrid, improves economic benefits, and makes the microgrid more sustainable in both economic and environmental terms.

[0133] An embodiment of the present application also discloses a power dispatching system for a microgrid based on the Internet of Things, including a processor, wherein 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] An embodiment of the present application further discloses a storage medium, in which a program is stored. When the program is executed by a processor, the steps of the power dispatching method for a microgrid based on the Internet of Things are implemented as described in any one of the above.

[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 cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify 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; If the difference between the comprehensive power generation data and the preset power generation reference data adjusts the mass-energy power generation 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 1, 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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