A Microgrid Measurement and Control Method and System Based on the Internet of Things

Abstract

The present invention discloses a method and system for measuring and controlling a microgrid based on the Internet of Things. The method includes: obtaining a power grid distribution map within an industrial park, collecting in real time the operation data of operating devices in each factory, obtaining the production plans and environmental data of each factory, where the operating devices include: surplus energy recovery devices, production capacity devices, and other devices; dividing the industrial park into multiple microgrids based on the power grid distribution map, analyzing the influence of the production plans and the environmental data on the electricity consumption demands of each factory, obtaining an electricity consumption demand prediction model for each operating device, putting the production plans into the corresponding electricity consumption demand prediction models, obtaining the electricity consumption demands of each factory for each future time period, comparing the electricity consumption demands with the electricity output within the corresponding microgrids, and formulating an energy supply and demand balance map between the microgrids. The present invention has the characteristics of reducing the dependence on fossil fuels and improving the energy utilization efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of microgrid measurement and control, and particularly to a microgrid measurement and control method and system based on the Internet of Things. Background Technique

[0002] A microgrid is a small, flexible and efficient power system that integrates multiple energy sources (such as solar energy, wind energy, energy storage devices, etc.) and can operate in a mode of connecting to the main grid or operating independently. Through an intelligent measurement, control and management system, the microgrid can monitor and adjust the production, storage and distribution of energy in real time to ensure the stability and reliability of power supply. It not only improves the energy utilization efficiency, reduces the dependence on traditional fossil fuels, but also enhances the disaster resistance and emergency response capabilities of the power system. However, the energy utilization efficiency of existing industrial parks still needs to be improved. In particular, insufficient attention is paid to the utilization of energy such as waste heat and waste cold, the utilization rate of industrial waste heat and waste cold is relatively low, the energy is not fully and comprehensively utilized, and there is still a great dependence on traditional fossil fuels. In addition, the prediction of the electricity demand of each factory in the existing industrial park is insufficient, resulting in a certain lag in the power distribution and management of each microgrid, and the actual electricity demand of each factory cannot be accurately met, thus affecting the energy utilization efficiency and economic benefits of the entire industrial park. Therefore, it is necessary to design a microgrid measurement and control method and system based on the Internet of Things that reduces the dependence on fossil fuels and improves the energy utilization efficiency. Summary of the Invention

[0003] The purpose of the present invention is to provide a microgrid measurement and control method and system based on the Internet of Things to solve the problems raised in the above background technique.

[0004] To solve the above technical problems, the present invention provides the following technical solution: A microgrid measurement and control method based on the Internet of Things, and the operation steps of the method include:

[0005] Step S1: Obtain the power grid distribution map in the industrial park, collect the operation data of the operating devices in each factory in real time, and obtain the production plans and environmental data of each factory, where the operating devices include: waste energy recovery devices, energy production devices, and other devices, and the other devices are other power-consuming devices in the factory operation except for the production devices;

[0006] Step S2: Divide the industrial park into multiple microgrids based on the power grid distribution map, analyze the influence of the production plans and the environmental data on the electricity demand of each factory, and obtain the electricity demand prediction model of each operating device;

[0007] Step S3: Put the production plan into the corresponding power demand prediction model, obtain the power demands of each factory in each future time period, compare the power demands with the power outputs within the corresponding microgrid, and formulate an energy supply-demand balance diagram among the microgrids.

[0008] Further, step S2 further includes the following steps:

[0009] Step S21: According to the power grid distribution map of the industrial park, combine the geographical locations, production scales, and energy consumption characteristics of the factories to divide the industrial park into multiple microgrids, and match each operating device in the industrial park to the corresponding factory;

[0010] Step S22: Associate the production devices with the corresponding production plans, analyze the power demands of each production device in different production plans and construct a power demand prediction model, analyze the deviation value between the power demand prediction model and the actual power demand, and adjust the power demand prediction model. The production plans include: historical production plans and future production plans;

[0011] Step S23: Obtain the shift schedule of the factory according to the historical production plan, classify the other devices in combination with the environmental data, and predict the power demands of each other device according to the classification.

[0012] Further, step S22 further includes the following steps:

[0013] Step S221: Classify the production capacity devices, divide the production capacity devices into recoverable production capacity devices and ordinary production capacity devices, and associate the surplus energy recovery device with the corresponding recoverable device. The recoverable production capacity device refers to a device that can generate surplus energy during operation and be recovered and generated electricity by the surplus energy recovery device;

[0014] Step S222: Sort out the production capacity devices that need to operate and the power consumption rules in different production plans according to the historical production plan, and obtain the power consumption curves of each production capacity device in each time period within the production cycle in different production plans according to the order volume and production cycle of the production plan;

[0015] Step S223: Smooth the curves, obtain the power consumption curves of different production plans under the same environment, and establish a power demand prediction model of the power consumption curve, the order volume, and the production cycle through support vector regression: , where represents the weight coefficient of the th support vector, represents the th support vector containing the order volume and production cycle, represents the input vector, represents the kernel function, represents the bias term of the model.

[0016] Further, the step S223 further includes the following steps:

[0017] Step S2231: Extract the production plans in different environments, put the corresponding order quantities and production cycles into the electricity demand prediction model to simulate the electricity consumption curve, calculate the deviation value from the actual electricity consumption curve. When the deviation value is within the deviation threshold range, it indicates that the electricity demand of the production capacity device is not affected by the environmental data, and the electricity demand prediction model remains unchanged;

[0018] Step S2232: When the deviation value exceeds the deviation threshold range, it indicates that the electricity demand of the production capacity device is affected by the environmental data. Train according to the characteristic differences between the deviation value and the environmental data to obtain the environmental energy efficiency factor of the impact of the environmental data on the electricity consumption curve, and integrate the environmental energy efficiency factor into the original electricity demand prediction model to obtain a new electricity demand prediction model;

[0019] Step S2231: When the production capacity device is a recoverable production capacity device, obtain the power generation curve of the surplus energy recovery device under the corresponding electricity consumption curve, obtain the surplus energy power generation prediction model according to the electricity demand of the associated recoverable production capacity device and the corresponding environmental data, subtract the corresponding power generation prediction model from the electricity demand prediction model of the recoverable production capacity device, and the recoverable production capacity device obtains a new electricity demand prediction model.

[0020] Further, the step S23 further includes the following steps:

[0021] Step S231: Classify the other devices according to the shift schedule and environmental data, and divide the other devices into stable other devices and unstable other devices. The unstable other devices refer to the devices used under the influence of the environmental data, and vice versa, they are the stable other devices;

[0022] Step S232: Forecast the daily electricity demand of the stable other devices, obtain the electricity consumption curve of the previous month before the prediction time of each stable other device, and take the daily similar electricity consumption curve as the electricity demand prediction model;

[0023] Step S233: Obtain the environmental data characteristic impact factor for the unstable other devices, formulate the environmental data impact usage value for the historical operation data of the unstable other devices. When the environmental data reaches the usage value, predict the unstable other devices, and construct an electricity demand prediction model according to the historical operation data.

[0024] Further, step S3 further includes the following steps:

[0025] Step S31: Put the future production plan into the power demand prediction models of each operating device, obtain the power demands of each operating device in each future time period, and obtain the power generation amounts of each new energy power generation device in the industrial park in each future time period according to the environmental data;

[0026] Step S32: Mark the prediction results of the power generation amount or the power demand in the form of a bar chart with a unified height at the corresponding positions on the power grid distribution map, and perform dynamic updates according to the real-time measurement and control data. The closer to the bottom of the bar chart, the later the predicted time period;

[0027] Step S33: Calculate the ratio A of the power generation output and consumption of each microgrid according to the predicted power generation amount or power demand in the bar chart. When A > 1, mark the corresponding time periods of the bar charts at each end of the microgrid in green. When A = 1, mark the corresponding time periods of the bar charts at each end of the microgrid in yellow. When A < 1, mark the corresponding time periods of the bar charts at each end of the microgrid in red, and formulate an energy supply and demand balance map between each microgrid;

[0028] Step S34: Calculate the total power generation amount and the total power demand in the industrial park, compare the total power generation amount with the total power demand. When the total power generation amount is greater than the total power demand, supply power from the first power generation end of the microgrid marked in green to the first and second power consumption ends of the microgrid marked in red during the corresponding time period. When the total power generation amount is less than the total power demand, request power supply from the energy storage system or the main power grid in the industrial park.

[0029] Further, the system includes a data acquisition module and a power demand model construction module:

[0030] The data acquisition module is used to obtain the power grid distribution map in the industrial park, collect the operation data of the operating devices in each factory in real time, and obtain the production plans and environmental data of each factory, where the operating devices include: waste energy recovery devices, production capacity devices, and other devices;

[0031] The power demand model construction module is used to divide the industrial park into multiple microgrids based on the power grid distribution map, analyze the influence of the production plan and the environmental data on the power demands of each factory, and obtain the power demand prediction models of each operating device.

[0032] Further, the system further includes an energy supply and demand balance scheduling module:

[0033] The energy supply - demand balance scheduling module is used to put the production plan into the corresponding power demand prediction model, obtain the power demands of each factory in each future time period, compare the power demands with the power output within the corresponding micro - grid, and formulate an energy supply - demand balance diagram among the micro - grids.

[0034] In the third aspect of the present application, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the electronic device implements the method as described in the first aspect of the present application.

[0035] In the fourth aspect of the present application, a computer - readable storage medium is provided. The computer - readable storage medium is used to store a computer program. When the computer program runs on a computer, the computer executes the method as described in the first aspect of the present application.

[0036] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: By dividing the industrial park into multiple micro - grids and predicting the power demands of each factory according to the production plan and environmental data, the present invention can more effectively balance the energy supply and demand among the micro - grids. Multiple methods are proposed to optimize the power demand prediction model, including adjusting model parameters according to historical data, considering the influence of environmental data, and special processing methods for new factories or new equipment. According to the predicted power generation and power demand, the present invention can formulate an energy supply - demand balance diagram and perform energy scheduling and optimal allocation according to the actual situation, thereby reducing the dependence on fossil fuels and improving the system stability. Description of the Drawings

[0037] The drawings are used to provide a further understanding of the present invention and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the present invention and do not constitute a limitation to the present invention. In the drawings:

[0038] Figure 1 It is a schematic flowchart diagram of a micro - grid measurement and control method based on the Internet of Things provided in Embodiment 1 of the present invention.

[0039] Figure 2 It is a schematic diagram of the module composition of a micro - grid measurement and control system based on the Internet of Things provided in Embodiment 2 of the present invention.

[0040] Figure 3 It is a schematic diagram of an electronic device according to an embodiment of the present application. Detailed Embodiments

[0041] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0042] This embodiment can be applied to the scenario of distributed energy management in industrial parks. This method can be executed by a microgrid measurement and control system based on the Internet of Things provided in this embodiment. Figure 1 It is a flowchart schematic diagram of a microgrid measurement and control method based on the Internet of Things provided in the first embodiment of the present invention. This method specifically includes the following steps:

[0043] Step S1: Obtain the power grid distribution map in the industrial park, collect the operation data of the operating devices in each factory in real time, and obtain the production plans and environmental data of each factory. The operating devices include: surplus energy recovery devices, production capacity devices, and other devices. The other devices are other power-consuming devices in the factory operation except the production devices.

[0044] Step S2: Divide the industrial park into multiple microgrids based on the power grid distribution map, analyze the influence of the production plans and the environmental data on the electricity demand of each factory, and obtain the electricity demand prediction model of each operating device.

[0045] Step S3: Put the production plans into the corresponding electricity demand prediction models, obtain the electricity demands of each factory in each future time period, compare the electricity demands with the electricity output in the corresponding microgrids, and formulate the energy supply and demand balance diagrams between the microgrids.

[0046] In a specific embodiment, by obtaining the power grid distribution map in the industrial park and collecting the operation data, production plans, and environmental data of the operating devices in each factory in real time, a comprehensive understanding of the energy status in the industrial park is achieved. This provides a detailed data basis for subsequent energy management and optimization, ensuring the accuracy and effectiveness of energy management.

[0047] In addition, based on the power grid distribution map, the industrial park is divided into microgrids, and the impact of production plans and environmental data on the electricity demand of each factory is deeply analyzed, and a prediction model for the electricity demand of each operating device is successfully obtained. This step not only improves the accuracy of energy prediction, but also can flexibly adjust the prediction model according to the production characteristics of different factories and environmental changes, providing strong support for formulating scientific energy management strategies. The production plan is put into the corresponding electricity demand prediction model, and the electricity demand of each factory in each future time period is accurately obtained, and compared with the electricity output in the corresponding microgrid, and an energy supply and demand balance map between each microgrid is formulated. Real-time monitoring and dynamic adjustment of the energy supply and demand situation in the industrial park are realized, which helps to timely discover and solve the problem of unbalanced energy supply and demand, improve energy utilization efficiency, and reduce energy costs.

[0048] In some preferred embodiments, the step S2 further includes the following steps:

[0049] Step S21: According to the power grid distribution map of the industrial park, combined with the geographical location, production scale and energy consumption characteristics of the factories, the industrial park is divided into multiple microgrids, and each operating device in the industrial park is matched to the corresponding factory;

[0050] Step S22: Associate the production device with the corresponding production plan, analyze the electricity demand of each production device in different production plans and construct an electricity demand prediction model, analyze the deviation value between the electricity demand prediction model and the actual electricity demand, and adjust the electricity demand prediction model. The production plan includes: historical production plan and future production plan;

[0051] Step S23: Obtain the shift schedule of the factory according to the historical production plan, classify the other devices in combination with the environmental data, and predict the electricity demand of each other device according to the classification.

[0052] Specifically, by dividing the industrial park into multiple microgrids according to the power grid distribution map of the industrial park, the geographical location of the factories, the production scale and the energy consumption characteristics, and matching each operating device, the refined division and optimal allocation of the energy management in the industrial park are realized, and the energy utilization efficiency is improved. By associating the production devices with the corresponding production plans, constructing an electricity demand prediction model, and analyzing and adjusting the deviation value between the prediction model and the actual electricity demand, the accurate prediction of the electricity demand of each production device under different production plans is realized, which helps the industrial park to better plan and dispatch power resources and reduce energy waste. By classifying other devices in combination with the shift plan of the historical production plan and the environmental data, and predicting the electricity demand of various devices, the flexibility and adaptability of the energy management in the industrial park are further improved, and the energy distribution strategy can be adjusted in a timely manner according to different situations to ensure the stable operation of each device and the reliability of the energy supply.

[0053] In some optional embodiments, the step S2 further further includes: when there is insufficient historical data of the new factories in the industrial park, performing similarity matching on each operating device in the new factories to obtain similar operating devices, and using the electricity demand prediction model of the similar operating devices to predict the electricity demand: , where represents the proportionality coefficient for adjusting the electricity demand of the similar operating devices to adapt to the new factories.

[0054] In some preferred embodiments, the step S22 further includes the following steps:

[0055] Step S221: Classify the production capacity devices, divide the production capacity devices into recoverable production capacity devices and ordinary production capacity devices, and associate the surplus energy recovery devices with the corresponding recoverable devices. The recoverable production capacity devices refer to those that can generate surplus energy during operation and be recovered and generated electricity by the surplus energy recovery devices;

[0056] Step S222: Sort out the production capacity devices that need to operate in different production plans and the electricity consumption rules according to the historical production plan, and obtain the electricity consumption curves of each production capacity device in different time periods within the production cycle in different production plans according to the order volume and production cycle of the production plan;

[0057] Step S223: Smooth the curves to obtain the electricity consumption curves of different production plans under the same environment, and establish an electricity demand prediction model of the electricity consumption curves, the order volume and the production cycle through support vector regression: , where represents the weight coefficient of the th support vector, represents the A support vector containing the order quantity and production cycle, represents the input vector, represents the kernel function, represents the bias term of the model.

[0058] Specifically, through the precise association of the classification of the production capacity device and the waste energy recovery device, the efficient recovery and utilization of waste energy are realized. At the same time, according to the historical production plans, the electricity consumption patterns of the production capacity devices in different production plans are sorted out, and combined with the order quantity and production cycle, a power demand prediction model is successfully constructed. Through the support vector regression method, this model smooths the electricity consumption curve and obtains the power demand predictions of different production plans under the same environment. This not only improves the refinement level of energy management, but also effectively predicts future power demands, providing strong support for the reasonable scheduling of production capacity devices and the optimal allocation of energy, thus realizing multiple benefits of energy conservation and emission reduction, improving energy utilization efficiency, and controlling production costs.

[0059] In some preferred embodiments, the step S223 further includes the following steps:

[0060] Step S2231: Extract the production plans under different environments, put the corresponding order quantity and production cycle into the power demand prediction model to simulate the electricity consumption curve, calculate the deviation value from the actual electricity consumption curve. When the deviation value is within the deviation threshold range, it indicates that the power demand of the production capacity device is not affected by the environmental data, and the power demand prediction model remains unchanged;

[0061] Step S2232: When the deviation value exceeds the deviation threshold range, it indicates that the power demand of the production capacity device is affected by the environmental data. Train according to the characteristic differences between the deviation value and the environmental data to obtain the environmental energy efficiency factor of the influence of the environmental data on the electricity consumption curve, and integrate the environmental energy efficiency factor into the original power demand prediction model to obtain a new power demand prediction model;

[0062] Step S2231: When the production capacity device is a recoverable production capacity device, obtain the power generation curve of the waste energy recovery device under the corresponding electricity consumption curve, obtain the waste energy power generation prediction model according to the power demand of the associated recoverable production capacity device and the corresponding environmental data, subtract the corresponding power generation prediction model from the power demand prediction model of the recoverable production capacity device, and the recoverable production capacity device obtains a new power demand prediction model.

[0063] Specifically, by extracting production plans under different environments and simulating the electricity consumption curves, the deviation values from the actual electricity consumption curves are calculated, achieving accurate judgment of the impact of environmental data on the electricity demand of production capacity devices. When the deviation values are within the set deviation threshold range, the effectiveness of the electricity demand prediction model is confirmed, unnecessary model modifications are avoided, and the prediction efficiency is improved. When the deviation values exceed the deviation threshold range, model training is carried out based on the characteristic differences between the deviation values and the environmental data, the environmental energy efficiency factors are obtained and integrated into the original electricity demand prediction model, thereby enhancing the model's adaptability to environmental changes and making the prediction results more accurate and reliable. In addition, for recoverable production capacity devices, by obtaining the power generation curves of the surplus energy recovery devices and combining the electricity demands of the associated recoverable production capacity devices and the corresponding environmental data, a surplus energy power generation prediction model is constructed. Subtracting the corresponding power generation prediction model from the electricity demand prediction model of the recoverable production capacity devices, a new electricity demand prediction model is obtained, realizing refined prediction of the electricity demand of the recoverable production capacity devices, improving the energy utilization efficiency, and contributing to the goal of energy conservation and emission reduction.

[0064] In some preferred embodiments, step S23 further includes the following steps:

[0065] Step S231: Classify the other devices according to the shift schedule and environmental data, and divide the other devices into stable other devices and unstable other devices. The unstable other devices refer to the devices that are used under the influence of the environmental data, and vice versa, they are the stable other devices;

[0066] Step S232: Predict the daily electricity demand of the stable other devices, obtain the electricity consumption curves of the previous month before the prediction time of each stable other device, and take the daily similar electricity consumption curves as the electricity demand prediction model;

[0067] Step S233: Obtain the environmental data characteristic influence factors for the unstable other devices, formulate the environmental data influence usage values for the historical operation data of the unstable other devices, and when the environmental data reaches the usage values, predict the unstable other devices and construct an electricity demand prediction model based on the historical operation data.

[0068] Specifically, by incorporating the future production plan into the electricity demand prediction model of each operating device and combining environmental data to predict the power generation of each new energy power generation device in the industrial park for each future time period, an accurate prediction of the electricity supply and demand situation in the industrial park is achieved. By marking the prediction results of power generation or electricity demand in the form of bar charts of the same height on the power grid distribution map and dynamically updating according to real-time measurement and control data, the intuitive visualization of the electricity supply and demand situation is realized. This visualization method enables managers to quickly grasp the electricity supply and demand status of each microgrid in the industrial park, facilitating the timely discovery and response to potential energy problems. According to the predicted power generation or electricity demand in the bar chart, calculate the ratio of power output to consumption of each microgrid, and mark the corresponding time periods with different colors according to the different ratios, and formulate an energy supply and demand balance map between each microgrid. An accurate assessment of the energy supply and demand balance in the industrial park is achieved, which helps managers formulate targeted energy dispatching strategies and optimize energy allocation. Finally, by calculating and comparing the total power generation and total electricity demand in the industrial park, flexibly adjust the energy supply strategy according to the comparison results. For example, when the total power generation is greater than the total electricity demand, give priority to using internal resources to achieve energy balance; when the total power generation is less than the total electricity demand, request power supply from the energy storage system or the main power grid in a timely manner. The flexibility and reliability of the energy supply in the industrial park are realized, ensuring the stable operation of production.

[0069] In some preferred embodiments, step S3 further includes the following steps:

[0070] Step S31: Put the future production plan into the electricity demand prediction model of each operating device, obtain the electricity demand of each operating device for each future time period, and obtain the power generation of each new energy power generation device in the industrial park for each future time period according to the environmental data;

[0071] Step S32: Mark the prediction results of the power generation or the electricity demand in the form of bar charts of the same height at the corresponding positions on the power grid distribution map and dynamically update according to real-time measurement and control data. The closer to the bottom of the bar chart, the later the predicted time period;

[0072] Step S33: According to the predicted power generation or electricity demand in the bar chart, calculate the ratio A of power output to consumption of each microgrid. When A > 1, mark the corresponding time periods of the bar charts at each end of the microgrid in green. When A = 1, mark the corresponding time periods of the bar charts at each end of the microgrid in yellow. When A < 1, mark the corresponding time periods of the bar charts at each end of the microgrid in red, and formulate an energy supply and demand balance map between each microgrid;

[0073] Step S34: Calculate the total power generation and total power demand in the industrial park, compare the total power generation with the total power demand. When the total power generation is greater than the total power demand, the first power generation end of the green - marked micro - grid provides power to the first power consumption end and the second power consumption end of the red - marked micro - grid during the corresponding time period. When the total power generation is less than the total power demand, request power supply from the energy storage system or the main grid in the industrial park.

[0074] Specifically, by classifying other devices according to the shift schedule and environmental data, and subdividing the devices into stable other devices and unstable other devices, accurate prediction of the power consumption demand of different types of devices is achieved. For stable other devices, by obtaining the power consumption curve of the previous month before the prediction time and selecting the power consumption curve with similar days as the power consumption demand prediction model, the accuracy and reliability of the prediction are improved. For unstable other devices, by obtaining the environmental data characteristic influence factors and formulating the environmental data influence usage value based on historical operation data, prediction is carried out when the environmental data reaches this usage value, and a power consumption demand prediction model is constructed according to historical operation data, thus realizing flexible prediction of the power consumption demand of devices affected by environmental data. This method comprehensively considers the influence of the shift schedule and environmental data, improves the comprehensiveness and accuracy of power consumption demand prediction, and helps to optimize energy distribution and management.

[0075] Based on the same inventive concept as the above - mentioned method embodiment, the embodiment of the present invention also provides an Internet - of - Things - based micro - grid measurement and control system. Figure 2 As shown in Figure 2 the schematic diagram of the module composition of an Internet - of - Things - based micro - grid measurement and control system provided by the embodiment of the present invention, the system includes a data acquisition module, a power consumption demand model construction module, and an energy supply - demand balance scheduling module:

[0076] The data acquisition module is used to obtain the power grid distribution map in the industrial park, collect the operation data of the operating devices in each factory in real - time, and obtain the production plans and environmental data of each factory, where the operating devices include: waste energy recovery devices, production capacity devices, and other devices.

[0077] The power consumption demand model construction module is used to divide the industrial park into multiple micro - grids based on the power grid distribution map, analyze the influence of the production plan and the environmental data on the power consumption demand of each factory, and obtain the power consumption demand prediction model of each operating device.

[0078] The energy supply - demand balance scheduling module is used to put the production plan into the corresponding power consumption demand prediction model, obtain the power consumption demand of each factory in each future time period, compare the power consumption demand with the power output within the corresponding micro - grid, and formulate the energy supply - demand balance map between each micro - grid.

[0079] It should be noted that although several units or subunits of the device are mentioned in the above detailed description, this division is merely exemplary and not mandatory. In fact, according to the embodiments of the present application, the features and functions of two or more units described above can be embodied in one unit. Conversely, the features and functions of one unit described above can be further divided and embodied by multiple units.

[0080] Based on the same inventive concept as the above method embodiments, an electronic device is further provided in the embodiments of the present application, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the electronic device implements the control method in the above embodiments.

[0081] In one embodiment, the electronic device may be a server. In this embodiment, the structure of the electronic device may be as Figure 3 shown, including a memory 2001, a communication module 2003, and one or more processors 2002.

[0082] The memory 2001 is used to store the computer program executed by the processor 2002. The memory 2001 may mainly include a program storage area and a data storage area. Among them, the program storage area may store an operating system and programs required to run the instant messaging function, etc.; the data storage area may store various instant messaging information and operation instruction sets, etc.

[0083] The memory 2001 may be a volatile memory, such as a random-access memory (RAM); the memory 2001 may also be a non-volatile memory, such as a read-only memory, a flash memory, a hard disk drive (HDD), or a solid-state drive (SSD); or the memory 2001 is any other medium that can be used to carry or store a desired computer program in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory 2001 may be a combination of the above memories.

[0084] The processor 2002 may include one or more central processing units (CPUs) or be a digital processing unit, etc. The processor 2002 is used to implement the above method for processing audio data when calling the computer program stored in the memory 2001.

[0085] The communication module 2003 is used to communicate with terminal devices and other servers.

[0086] In the embodiments of the present application, the specific connection medium between the above-mentioned memory 2001, communication module 2003 and processor 2002 is not limited. In the embodiments of the present application Figure 3 it is described that the memory 2001 and the processor 2002 are connected through a bus 2004, and the bus 2004 is Figure 3 described by an arrow. The connection manners between other components are only for illustrative purposes and are not restrictive. The bus 2004 can be divided into an address bus, a data bus, a control bus, etc. For the convenience of description, Figure 3 only one arrow is used for description in it, but it does not describe that there is only one bus or one type of bus.

[0087] Based on the same inventive concept as the above method embodiments, an embodiment of the present invention further provides a computer-readable storage medium, which is used to store a computer program. When the computer program runs on a computer, the electronic device is enabled to implement the control method in the above embodiments. The computer-readable storage medium can be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (non-exhaustive list) of the readable storage medium include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0088] Based on the same inventive concept as the above method embodiments, an embodiment of the present invention further provides a computer program product, which includes a computer program. When the program product runs on an electronic device, the computer program is used to enable the electronic device to execute the steps in the control method according to various exemplary embodiments of the present application described above in this specification. The program product can adopt any combination of one or more readable media. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for realizing the functions specified in Figure 1 one process or multiple processes and / or Figure 1 one block or multiple blocks.

[0089] Although the preferred embodiments of the present application have been described, additional changes and modifications can be made to these embodiments by those skilled in the art once they learn of the basic inventive concept. Therefore, the appended claims are intended to be construed to cover the preferred embodiments as well as all changes and modifications that fall within the scope of the present application.

Claims

1. A measurement and control method for a microgrid based on the Internet of Things, characterized in that: Obtain the power grid distribution map within the industrial park, collect the operation data of the operating devices in each factory in real time, and obtain the production plans and environmental data of each factory, where the operating devices include: surplus energy recovery devices, production capacity devices, and other devices, and the other devices are other power-consuming devices in the factory operation except for the production devices; Based on the power grid distribution map, divide the industrial park into multiple microgrids, analyze the influence of the production plans and the environmental data on the power consumption demands of each factory, and obtain the power consumption demand prediction models for each operating device; Extract the production plans under different environments, put the corresponding order quantities and production cycles into the power consumption demand prediction model to simulate the power consumption curve, and calculate the deviation value from the actual power consumption curve. When the deviation value is within the deviation threshold range, it means that the power consumption demand of the production capacity device is not affected by the environmental data, and the power consumption demand prediction model remains unchanged; When the deviation value exceeds the deviation threshold range, it means that the power consumption demand of the production capacity device is affected by the environmental data. Train according to the characteristic differences between the deviation value and the environmental data to obtain the environmental energy efficiency factor of the influence of the environmental data on the power consumption curve, and integrate the environmental energy efficiency factor into the original power consumption demand prediction model to obtain a new power consumption demand prediction model; When the production capacity device is a recoverable production capacity device, obtain the power generation curve of the surplus energy recovery device under the corresponding power consumption curve, obtain the surplus energy power generation prediction model according to the power consumption demand of the associated recoverable production capacity device and the corresponding environmental data, and subtract the corresponding power generation prediction model from the power consumption demand prediction model of the recoverable production capacity device to obtain a new power consumption demand prediction model for the recoverable production capacity device; Put the production plans into the corresponding power consumption demand prediction models, obtain the power consumption demands of each factory in each future time period, compare the power consumption demands with the power output within the corresponding microgrid, and formulate the energy supply and demand balance map between the microgrids.

2. The method for measuring and controlling a microgrid based on the Internet of Things according to claim 1, wherein: The dividing the industrial park into multiple microgrids based on the power grid distribution map, analyzing the influence of the production plans and the environmental data on the power consumption demands of each factory, and obtaining the power consumption demand prediction models for each operating device includes: According to the power grid distribution map of the industrial park, combined with the geographical location, production scale and energy consumption characteristics of the factories, divide the industrial park into multiple microgrids, and match each operating device in the industrial park to the corresponding factory; Associate the production devices with the corresponding production plans, analyze the power consumption demands of each production device in different production plans and construct a power consumption demand prediction model, analyze the deviation value between the power consumption demand prediction model and the actual power consumption demand, and adjust the power consumption demand prediction model. The production plans include: historical production plans and future production plans; Obtain the shift schedule of the factory according to the historical production plan, classify the other devices in combination with the environmental data, and predict the power consumption demands of each other device according to the classification.

3. A microgrid measurement and control method based on the Internet of Things according to claim 2, characterized in that: Associating the production devices with corresponding production plans, analyzing the power consumption requirements of each production device in different production plans and constructing a power consumption requirement prediction model, analyzing the deviation value between the power consumption requirement prediction model and the actual power consumption, and adjusting the power consumption requirement prediction model. The production plans include historical production plans and future production plans, including: Classifying the production capacity devices, dividing the production capacity devices into recoverable production capacity devices and ordinary production capacity devices, and associating the surplus energy recovery device with the corresponding recoverable device. The recoverable production capacity device refers to a device that can generate surplus energy during operation and be recovered and generated electricity by the surplus energy recovery device; Sorting out the production capacity devices that need to operate and the power consumption rules in different production plans according to the historical production plan, and obtaining the power consumption curves of each production capacity device in different time periods within the production cycle in different production plans according to the order quantity and production cycle of the production plan; Smooth the curve to obtain the electricity consumption curves of different production plans under the same environment, and establish a prediction model for the electricity consumption demand of the electricity consumption curve, the order volume, and the production cycle through support vector regression: , where represents the weight coefficient of the i-th support vector, represents the i-th support vector including the order volume and the production cycle, represents the input vector, represents the kernel function, represents the bias term of the model.

4. The method for measuring and controlling a microgrid based on the Internet of Things according to claim 3, characterized in that: Obtaining the shift schedule of the factory according to the historical production plan, classifying the other devices in combination with the environmental data, and predicting the power consumption requirements of each other device according to the classification, including: Classifying the other devices according to the shift schedule and environmental data, dividing the other devices into stable other devices and unstable other devices. The unstable other devices refer to the devices used under the influence of the environmental data. Otherwise, they are the stable other devices; Predicting the daily power consumption requirements of the stable other devices, obtaining the power consumption curves of each stable other device in the previous month before the prediction time, and taking the power consumption curves with similar days as the power consumption requirement prediction model; Obtaining the environmental data characteristic influence factors for the unstable other devices, formulating the environmental data influence usage values for the historical operation data of the unstable other devices, predicting the unstable other devices when the environmental data reaches the usage value, and constructing a power consumption requirement prediction model according to the historical operation data.

5. The microgrid measurement and control method based on the Internet of Things according to claim 4, characterized in that: Putting the production plan into the corresponding power consumption requirement prediction model, obtaining the power consumption requirements of each factory in future time periods, comparing the power consumption requirements with the power output within the corresponding microgrid, and formulating an energy supply and demand balance diagram between each microgrid, including: Putting the future production plan into the power consumption requirement prediction models of each operating device, obtaining the power consumption requirements of each operating device in future time periods, and obtaining the power generation of each new energy power generation device in the industrial park in future time periods according to the environmental data; Marking the prediction results of the power generation or the power consumption requirements in the form of bar charts with a unified height at the corresponding positions on the power grid distribution map, and dynamically updating according to the real-time measurement and control data. The closer to the bottom of the bar chart, the later the predicted time period; Calculate the ratio of power output and consumption for each microgrid according to the predicted power generation or power consumption demand in the bar chart , when , mark the corresponding time period of the bar chart at each end of the microgrid in green. When , mark the corresponding time period of the bar chart at each end of the microgrid in yellow. When , mark the corresponding time period of the bar chart at each end of the microgrid in red, and formulate an energy supply and demand balance chart between each microgrid; Calculate the total power generation and total power consumption demand within the industrial park, compare the total power generation with the total power consumption demand. When the total power generation is greater than the total power consumption demand, the first power generation end of the microgrid marked green within the corresponding time period supplies power to the first power consumption end and the second power consumption end of the microgrid marked red. When the total power generation is less than the total power consumption demand, request power supply from the energy storage system or the main grid within the industrial park.

6. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it causes the electronic device to implement the method according to any one of claims 1 to 5.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store a computer program. When the computer program runs on a computer, it causes the computer to execute the method according to any one of claims 1 to 5.

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