Distributed energy scheduling method based on Internet of Things edge computing enabling

Abstract

The invention relates to the technical field of energy scheduling, in particular to a distributed energy scheduling method based on Internet of Things edge calculation enabling, and the method comprises the steps: collecting the actual power utilization information of each power utilization region; determining an average consumption ratio based on the actual power consumption and the expected power consumption of each power consumption area; determining the expected power consumption of each power consumption area based on the actual power consumption of each power consumption area in each analysis period, and distributing and dispatching electric energy to each power consumption area according to the energy dispatching proportion of each power consumption area; and determining whether the operation parameters of the power supply layer are qualified based on the average consumption ratio, and when the operation parameters of the power supply layer are judged to be abnormal, adjusting power supply scheduling data based on the consumption difference quantity, including identifying an abnormal region based on actual power consumption and correcting an energy scheduling proportion for the abnormal region based on an abnormal region influence parameter and an energy overflow parameter. Or correcting the expected average power consumption. Accurate prediction of expected electricity consumption and accurate determination of energy scheduling data are realized.

Description

Technical Field

[0001] The present invention relates to the field of energy data processing technology, and in particular to a distributed energy scheduling method enabled by edge computing of the Internet of Things. Background Art

[0002] With the continued growth of global energy demand and increasing attention to sustainable energy utilization, distributed energy systems (DESs), as an efficient and flexible energy supply method, are gradually becoming a development trend in the future energy sector. DESs deploy power generation equipment, such as solar photovoltaic panels, wind turbines, and small gas turbines, near user locations. This enables local energy production and utilization, reducing transmission losses and improving energy efficiency. However, the widespread use of DESs also brings a series of challenges, particularly energy scheduling.

[0003] Distributed energy systems involve a vast amount of energy data, such as actual electricity consumption in each electricity-consuming area, the power generated by power generation equipment, and the amount of energy stored in the energy storage layer. This data is diverse, real-time, and massive. Electricity consumption and demand vary across different electricity-consuming areas. Therefore, effective processing and analysis of this complex data is fundamental to achieving precise energy scheduling.

[0004] IoT technology enables real-time data collection from various power consumption areas and power generation equipment. Smart meters are installed in each power consumption area to record actual power consumption in real time and transmit this data to a data center via wireless communication. The data center then stores and manages this massive amount of data for subsequent analysis and processing.

[0005] Energy scheduling in distributed energy systems requires consideration of multiple factors, such as electricity demand, power generation capacity, and energy storage status. Because these factors are inherently uncertain, accurate forecasting is crucial for optimizing energy scheduling. Data forecasting can help dispatchers understand the power demand of each power-consuming area and the power generation status of power generation equipment in advance, thereby rationally arranging energy production and distribution.

[0006] Chinese patent publication number: CN111600304B, discloses a building power dispatching method, device and equipment, including classifying the first typical load curves of typical electrical equipment to obtain second typical load curves under different power consumption modes, and then establishing a mathematical model based on the second typical load curve, thereby improving the accuracy of the description of the building user's power load, and then calculating the power dispatching strategy of each typical electrical equipment under different power consumption modes through an adaptive dynamic programming method, and finally calculating the total dispatching data of the typical electrical equipment based on the first power dispatching data and the first preset weight vector under different power consumption modes; it can be seen that the above technical solution has the following problems: it does not take into account the real-time collection and analysis of the electricity usage of each building, resulting in low accuracy of the predicted expected power consumption, which affects the energy utilization efficiency. Summary of the Invention

[0007] To this end, the present invention provides a distributed energy scheduling method enabled by edge computing of the Internet of Things, so as to overcome the problem that the existing technology does not take into account the real-time collection and analysis of the electricity usage of each building, resulting in low accuracy of the predicted expected electricity consumption and affecting the energy utilization efficiency.

[0008] To achieve the above objectives, the present invention provides a distributed energy scheduling method enabled by edge computing of the Internet of Things, comprising: S1, collecting actual electricity consumption information of each electricity consumption area; S2, periodically determining an average consumption ratio based on the actual power consumption and expected power consumption of each power consumption area; S3, determining the expected power consumption of each power consumption area based on the actual power consumption of each power consumption area in each analysis period, and the energy dispatch ratio for each power consumption area, and distributing and dispatching power to each power consumption area; S4, determining whether the operating parameters of the power supply layer are qualified based on the average consumption ratio, including: Determine whether the operating parameters of the power supply layer are qualified and continue to use the current power supply scheduling data for energy scheduling, or, when it is determined that the operating parameters of the power supply layer are abnormal, adjusting the power supply dispatch data based on the consumption difference, including identifying the abnormal area based on the actual power consumption and correcting the energy dispatch ratio for the abnormal area based on the abnormal area impact parameter and the energy overflow parameter, or correcting the expected average power consumption; When the adjustment of the energy scheduling ratio is completed, the operating parameters of the power supply layer are re-determined to be qualified based on the average consumption ratio. When the operating parameters of the power supply layer are still determined to be abnormal, the expected average power consumption of the next analysis cycle is adjusted to the corresponding value based on the change in power consumption.

[0009] Furthermore, the process of periodically determining whether the operating parameters of the power supply layer are qualified based on the average consumption ratio includes: It is used to calculate the ratio of actual power consumption to expected average power consumption of a single power consumption area, calculate the average power consumption ratio of each power consumption area, and obtain the average consumption ratio; The expected average electricity consumption is the average of the expected electricity consumption in each electricity consumption area; If the average consumption ratio is less than or equal to the preset average consumption ratio, the operating parameters of the power supply layer are determined to be qualified, and the current power supply scheduling data is continuously used for energy scheduling; If the average consumption ratio is greater than the preset average consumption ratio, it is determined that the operating parameters of the power supply layer are abnormal, and the power supply scheduling data is adjusted based on the consumption difference.

[0010] Furthermore, adjusting the power supply scheduling data based on the consumption difference includes: The variance of each calculated power ratio is used to determine the consumption difference; If the consumption difference is less than or equal to the preset consumption difference, the expected average power consumption of the next analysis period is adjusted to the corresponding value based on the power consumption change; If the consumption difference is greater than the preset consumption difference, the abnormal area is identified based on the actual power consumption, and the energy scheduling ratio for the abnormal area is corrected based on the abnormal area impact parameter and the energy overflow parameter.

[0011] Furthermore, the process of identifying abnormal areas based on actual power consumption includes: Mark the power consumption area where the ratio of actual power consumption to the corresponding expected power consumption is greater than the preset predicted ratio as an abnormal area; The process of correcting the energy dispatch ratio for a single abnormal area based on the energy overflow parameter includes: The energy spillover parameter is the difference between the actual electricity consumption of a single electricity consumption area and the expected average electricity consumption of all electricity consumption areas; The increase in the energy dispatch ratio of a single abnormal area is positively correlated with the energy spillover parameter.

[0012] Furthermore, the process of correcting the energy dispatch ratio for each abnormal area based on the abnormal area impact parameter includes: The ratio of the number of statistically abnormal areas to the total number of power consumption areas is determined as the abnormal area impact parameter; The reduction in the energy dispatch ratio for abnormal areas is positively correlated with the impact parameters of abnormal areas.

[0013] Furthermore, when the adjustment of the energy scheduling ratio is completed, the operating parameters of the power supply layer are re-determined to be qualified based on the average consumption ratio. When the operating parameters of the power supply layer are still determined to be abnormal, the expected average power consumption of the next analysis cycle is adjusted to the corresponding value based on the change in power consumption.

[0014] Furthermore, the process of adjusting the expected average power consumption of the next analysis period to a corresponding value based on the power consumption change includes: Based on the actual average power consumption obtained in each historical analysis period, an actual average power consumption curve is drawn, and the slope of the curve in the current analysis period is determined as the power consumption change; The increase in the expected average electricity consumption in the next analysis period is positively correlated with the change in electricity consumption.

[0015] Furthermore, when the adjustment for the expected average power consumption is completed, it is determined whether to modify the power limit of each power consumption area based on the energy consumption parameter and the actual power consumption, wherein: Solve the ratio of the electric energy consumed by the storage layer for storing electric energy in a single analysis cycle to the total electric energy of the storage layer at the beginning of the single analysis cycle to obtain the energy consumption parameter; The reduction in the power limit of each power consumption area is positively correlated with the energy consumption parameters.

[0016] Furthermore, based on the actual power consumption, the power limit of the power application area is adjusted to a corresponding value, wherein, The increase in power limit is negatively correlated with actual power consumption.

[0017] Compared to existing technologies, the present invention offers the advantage of not only relying on single power consumption data but also comprehensively considering multiple data indicators, including average consumption ratio, consumption variance, abnormal area impact parameters, energy overflow parameters, and power consumption variation. Multi-dimensional data is used to determine the changes and characteristics of power consumption from different perspectives. Comprehensive analysis of this multi-dimensional data provides a more comprehensive understanding of the changing trends in power demand, thereby improving the accuracy of expected power consumption forecasts. Within each analysis cycle, subsequent expected power consumption is adjusted based on the comparison between actual and expected power consumption. When operating parameters at the power supply layer are determined to be abnormal, corresponding scheduling data adjustments are made based on various data indicators, including correcting the energy scheduling ratio and adjusting the expected average power consumption. Through dynamic adjustment and feedback, changes in power consumption are promptly responded to, ensuring that the expected power consumption forecast is more consistent with actual conditions, thereby improving forecast accuracy. The identification and processing of abnormal areas, as well as the response measures for various abnormal situations, enhance the robustness of the expected power consumption forecast. When a power consumption area is identified as an abnormal area where the ratio of actual power consumption to the corresponding expected power consumption is greater than the preset forecast ratio, the energy scheduling ratio for the abnormal area is corrected based on the abnormal area impact parameters and energy overflow parameters, and the abnormal situation is analyzed and processed in a targeted manner, further improving the accuracy of the forecast data.

[0018] Furthermore, by improving the accuracy of expected electricity consumption forecasts, we can provide more accurate predictions of future electricity demand across various power consumption areas, thereby providing more precise energy transmission data and improving the efficiency of energy distribution across these areas. Accurately forecasting expected electricity consumption enables rational energy reserve planning. Determining the appropriate amount of energy reserves based on actual demand reduces energy storage costs while improving overall energy utilization efficiency.

[0019] Furthermore, the analysis layer periodically determines whether the operating parameters of the power supply layer are qualified based on the average consumption ratio. The average consumption ratio represents the overall degree of match between actual power consumption and expected average power consumption in each power consumption area. When the average consumption ratio is less than or equal to the preset average consumption ratio, the operating parameters of the power supply layer are able to meet power demand. By monitoring the average consumption ratio and promptly identifying whether the operating parameters of the power supply layer need adjustment, the rationality of energy allocation is ensured. This timely identification of problems in the power supply layer's operation avoids energy waste or power shortages caused by improper energy allocation, thereby improving the efficiency of energy management.

[0020] Furthermore, power supply scheduling data is adjusted based on consumption variance, which reflects the degree of dispersion between the actual power consumption and the expected average power consumption in each power consumption area. When the average consumption ratio is less than or equal to the preset average consumption ratio, the consumption variance is small, and the power consumption in each power consumption area is relatively stable. The expected average power consumption is adjusted to adapt to the overall power consumption trend. When the average consumption ratio is less than or equal to the preset average consumption ratio, the consumption variance is large. At this time, due to the presence of abnormal power consumption areas, the degree of deviation between the actual power consumption and the expected power consumption in each power consumption area varies greatly. Adopting different adjustment strategies for different power consumption situations improves the flexibility and accuracy of energy scheduling, optimizes energy distribution, and further improves energy utilization efficiency.

[0021] Furthermore, the analysis layer marks abnormal areas and adjusts the energy dispatch ratio. Abnormal areas are areas with abnormal power consumption and adjusts the energy dispatch ratio. The energy overflow parameter represents electricity demand that exceeds expectations. The abnormal area impact parameter reflects the degree of impact of abnormal areas on overall energy allocation. Promptly identifying and addressing abnormal power consumption areas prevents abnormal power consumption in individual areas from affecting the rationality of overall energy allocation and ensures that the power needs of all power-consuming areas are met. This resolves the problem of abnormal power consumption in individual areas, improves the fairness and rationality of energy allocation, and ensures normal power consumption in all power-consuming areas. The expected average power consumption is adjusted based on the change in power consumption, which reflects the changing trend of power demand. By analyzing the slope of the historical actual average power consumption curve, future changes in power demand are predicted and adjusted accordingly. The change in power consumption reflects the growth trend of power demand. Adjusting the expected average power consumption based on the changing trend of power demand allows energy allocation to better adapt to future power demand and avoid energy shortages or waste. This allows energy allocation to keep pace with changes in power demand, improving the adaptability and stability of energy supply.

[0022] Furthermore, the analysis layer adjusts the power limits for each power consumption area based on energy consumption parameters and actual power consumption. Energy consumption parameters reflect the energy consumption of the energy storage layer. When energy consumption parameters are high, the energy reserves of the energy storage layer are insufficient. In this case, the power limits for each power consumption area are lowered to reduce energy consumption. When energy reserves in the energy storage layer are insufficient, energy consumption is controlled by adjusting the power limits to ensure a sustainable energy supply. Furthermore, the power limits are adjusted based on actual power consumption to avoid energy waste or power shortages caused by excessively high or low power limits. When energy reserves are insufficient, energy consumption is controlled by adjusting the power limits, ensuring a sustainable energy supply and improving energy efficiency. The power limits for each building are adjusted based on the actual energy usage of each power consumption area. Reasonable adjustment of the power limits for each power consumption area ensures a sustainable energy supply and optimizes energy management. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 A block diagram of a distributed energy scheduling system that enables IoT edge computing according to an embodiment of the present invention; Figure 2 A flowchart of the steps of a distributed energy scheduling method for enabling edge computing of the Internet of Things according to an embodiment of the present invention. DETAILED DESCRIPTION

[0024] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are merely used to explain the present invention and are not intended to limit the present invention.

[0025] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0026] Furthermore, it should be noted that, in the description of the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0027] See also Figure 1 As shown in FIG, it is a module block diagram of a distributed energy scheduling system enabled by edge computing of the Internet of Things according to an embodiment of the present invention. The distributed energy scheduling system enabled by edge computing of the Internet of Things according to the present invention includes: A power generation layer, which includes several power generation devices; an electricity storage layer connected to the power generation layer and comprising a plurality of electricity storage devices for storing electrical energy; A power-consuming layer, which is connected to the power storage layer and the power generation layer respectively, and includes a plurality of power-consuming areas, each of which includes a plurality of power-consuming terminals; A collection layer, which is connected to the power consumption layer and includes a plurality of power collection devices respectively arranged in each power consumption area for periodically collecting the actual power consumption of each power consumption area; a power supply layer, which is respectively connected to the power generation layer, the power storage layer, the power consumption layer, and the collection layer, and is used to allocate and schedule the power of the power storage layer and the power generation layer, and to determine the expected power consumption of each power consumption area based on the actual power consumption of each power consumption area in each analysis period, and the energy scheduling ratio for each power consumption area; The analysis layer is connected to the collection layer and the power supply layer respectively, and is used to determine whether the operating parameters of the power supply layer are qualified based on the average consumption ratio, and when it is determined that the operating parameters of the power supply layer are abnormal, adjust the power supply scheduling data based on the consumption difference, including adjusting the expected average power consumption to the corresponding value, or correcting the energy scheduling ratio for the abnormal area.

[0028] See also Figure 2 As shown, it is a flowchart of the steps of the distributed energy scheduling method enabled by edge computing of the Internet of Things according to an embodiment of the present invention; the distributed energy scheduling method enabled by edge computing of the Internet of Things according to the present invention includes: S1, collecting actual electricity consumption information of each electricity consumption area; S2, periodically determining an average consumption ratio based on the actual power consumption and expected power consumption of each power consumption area; S3, determining the expected power consumption of each power consumption area based on the actual power consumption of each power consumption area in each analysis period, and the energy dispatch ratio for each power consumption area, and distributing and dispatching power to each power consumption area; S4, determining whether the operating parameters of the power supply layer are qualified based on the average consumption ratio, including: When it is determined that the operating parameters of the power supply layer are abnormal, the power supply dispatch data is adjusted based on the consumption difference, including adjusting the expected average power consumption to the corresponding value, or correcting the energy dispatch ratio for the abnormal area; Alternatively, determine that the operating parameters of the power supply layer are qualified, and continue to use the current power supply scheduling data for energy scheduling.

[0029] Specifically, there is no limitation on the specific structure of the power generation equipment, which may include rooftop photovoltaic and building gas units. This is existing technology and will not be elaborated on.

[0030] Specifically, the specific structure of the electric energy collection device is not limited and may include a voltmeter and an ammeter.

[0031] Specifically, there is no limitation on the specific method of predicting the expected electricity consumption for each electricity consumption area in the next analysis period based on the actual electricity consumption of each electricity consumption area in each analysis period. It can be predicted based on historical data and related algorithms, and can be determined by exponential smoothing method. The actual electricity consumption in different periods is given corresponding weights, where the weight of recent data is greater than that of long-term data. This is an existing technology and will not be repeated here.

[0032] Specifically, to determine the energy dispatch ratio of each power consumption area, energy can be allocated according to the ratio of the expected power consumption of each power consumption area to the expected total power consumption of all power consumption areas, which will not be elaborated here.

[0033] Specifically, the power supply layer determines the expected power consumption for each power consumption area based on the actual power consumption of each power consumption area during each analysis period. The power supply layer determines the energy dispatch ratio for each power consumption area based on the power generation capacity of the power generation layer, the power storage capacity of the power storage layer, and the expected power consumption of each power consumption area. This ratio represents the relative share of electricity each power consumption area obtains from the power generation layer and the power storage layer. Based on the expected power consumption of each power consumption area and the energy dispatch ratio, the power supply layer sends instructions to the power generation equipment in the power generation layer to adjust the power generation power. If the expected power consumption increases, the power supply layer will require the power generation equipment to increase the power generation power; otherwise, the power generation power will be reduced.

[0034] Specifically, determining expected power consumption provides a basis for adjusting the power generation capacity of the power generation equipment in the power generation layer. Accurately forecasting power consumption in each power consumption area enables the power generation layer to rationally plan power generation, avoiding energy waste from overgeneration or power shortages caused by undergeneration. Determining expected power consumption also enables the power supply layer to rationally manage the power consumption of the power storage layer. Based on expected power consumption, the power supply layer determines the charging and discharging strategy of the power storage layer, ensuring that the power storage layer has sufficient power to release during peak power consumption periods and can store excess energy during low power consumption periods.

[0035] Specifically, by determining the energy dispatch ratio, we optimize the resource allocation of the power generation layer and the power storage layer. According to the power demand of each power consumption area, we adjust the energy dispatch ratio to make efficient use of power resources.

[0036] Specifically, actual electricity consumption represents the energy consumption of each power-consuming area over a specific time period. The power supply layer determines expected electricity consumption and energy dispatch ratios. Based on historical electricity consumption data and current electricity consumption trends, it predicts the future electricity demand of each power-consuming area to rationally allocate electricity between the power generation layer and the storage layer. Expected electricity consumption is an estimate of the future electricity consumption of each power-consuming area, and the energy dispatch ratio determines each power-consuming area's share of the total energy distribution. Planning energy distribution in advance avoids energy shortages or waste and ensures that the electricity needs of each power-consuming area are met. This achieves rational energy allocation, improves the stability and reliability of energy supply, and reduces energy waste.

[0037] It should be pointed out that the data in this embodiment are all empirical values ​​determined based on historical electricity consumption data and energy supply capacity. The preset average consumption ratio is determined by analyzing the distribution of the ratio of actual electricity consumption in each electricity consumption area to the expected average electricity consumption in the historical data, and can be taken as the average value or a value near the median of the ratio in the historical data. The preset consumption difference is determined based on the fluctuation of electricity consumption in each electricity consumption area, and the fluctuation of electricity consumption is positively correlated with the setting of the preset consumption difference. The smaller the value of the preset prediction ratio is selected, the higher the accuracy of the electricity consumption forecast will be. The method of determining the setting of each preset parameter according to the method of the present invention can be to select the value with the highest proportion as the preset standard parameter according to the data distribution, use weighted summation to use the obtained value as the preset standard parameter, or other selection methods, as long as the method of the present invention can clearly define the different specific situations in the single judgment process through the obtained values.

[0038] Specifically, the analysis layer is used to periodically determine whether the operating parameters of the power supply layer are qualified based on the average consumption ratio, including: It is used to calculate the ratio of actual power consumption to expected average power consumption in a single power consumption area, and calculate the average of each power ratio to obtain the average consumption ratio; The expected average electricity consumption is the average of the expected electricity consumption in each electricity consumption area; If the average consumption ratio is less than or equal to the preset average consumption ratio, the operating parameters of the power supply layer are determined to be qualified, and the current power supply scheduling data is continuously used for energy scheduling; If the average consumption ratio is greater than the preset average consumption ratio, it is determined that the operating parameters of the power supply layer are abnormal, and the power supply scheduling data is adjusted based on the consumption difference.

[0039] Specifically, the preset average consumption ratio is selected within the interval [1, 1.05].

[0040] The analysis layer periodically determines whether the operating parameters of the power supply layer are adequate based on the average consumption ratio. The average consumption ratio represents the overall degree of alignment between actual power consumption and expected average power consumption in each power-consuming area. When the average consumption ratio is equal to or less than the preset average consumption ratio, the operating parameters of the power supply layer are sufficient to meet power demand. By monitoring the average consumption ratio and promptly identifying any need for adjustment to the operating parameters of the power supply layer, the rationality of energy allocation is ensured. This allows for the timely identification of operational issues at the power supply layer, preventing energy waste or power shortages caused by inappropriate energy allocation, and improving energy management efficiency.

[0041] Specifically, the analysis layer is used to adjust the power supply scheduling data based on the consumption difference, including: The variance of each calculated power ratio is used to determine the consumption difference; If the consumption difference is less than or equal to the preset consumption difference, the expected average power consumption of the next analysis period is adjusted to the corresponding value based on the power consumption change; If the consumption difference is greater than the preset consumption difference, the abnormal area is identified based on the actual power consumption, and the energy scheduling ratio for the abnormal area is corrected based on the abnormal area impact parameter and the energy overflow parameter.

[0042] Specifically, the preset consumption difference is selected within the interval [0.1, 0.16].

[0043] Specifically, the ratio of the amount of electricity planned to be delivered from the electricity storage layer to a single electricity consumption area within a preset analysis period to the total amount of electricity in the electricity storage layer is determined as the energy scheduling ratio for the single electricity consumption area.

[0044] Specifically, power supply scheduling data is adjusted based on consumption variance, which reflects the degree of dispersion between the actual power consumption of each power consumption area and the expected average power consumption. When the average consumption ratio is less than or equal to the preset average consumption ratio, the consumption variance is small, and the power consumption of each power consumption area is relatively stable. The expected average power consumption is adjusted to adapt to the overall power consumption trend. When the average consumption ratio is greater than the preset average consumption ratio, the consumption variance is large. At this time, due to the presence of abnormal power consumption areas, the degree of deviation between the actual power consumption of each power consumption area and the expected power consumption varies greatly. Adopting different adjustment strategies for different power consumption situations improves the flexibility and accuracy of energy scheduling, optimizes energy distribution, and further improves energy utilization efficiency.

[0045] Specifically, the analysis layer is used to mark the power consumption area where the ratio of actual power consumption to the corresponding expected power consumption is greater than the preset predicted ratio as an abnormal area; The analysis layer is used to modify the energy dispatch ratio for a single abnormal area based on the energy overflow parameter, wherein: The energy spillover parameter is the difference between the actual electricity consumption of a single electricity consumption area and the expected average electricity consumption of all electricity consumption areas; The increase in the energy dispatch ratio of a single abnormal area is positively correlated with the energy spillover parameter.

[0046] In this embodiment, optionally, comparing the energy overflow parameter with a first preset overflow parameter and a second preset overflow parameter; If the energy overflow parameter is less than or equal to the first preset overflow parameter, the energy dispatch ratio of the single abnormal area is adjusted to 1.12 times the corresponding initial energy dispatch ratio; If the energy overflow parameter is less than or equal to the second preset overflow parameter and greater than the first preset overflow parameter, the energy dispatch ratio of the single abnormal area is adjusted to 1.21 times the corresponding initial energy dispatch ratio; If the energy overflow parameter is greater than the second preset overflow parameter, the energy dispatch ratio of the single abnormal area is adjusted to 1.31 times the corresponding initial energy dispatch ratio; The first preset overflow parameter is 1.15, and the second preset overflow parameter is 1.25.

[0047] Specifically, the preset prediction ratio is 1.

[0048] Specifically, the analysis layer is used to modify the energy scheduling ratio for each abnormal area based on the abnormal area impact parameter; The analysis layer is used to determine the ratio of the number of statistical abnormal areas to the total number of power consumption areas as the abnormal area impact parameter; The reduction in the energy dispatch ratio for abnormal areas is positively correlated with the impact parameters of abnormal areas.

[0049] In this embodiment, optionally, Comparing the abnormal area impact parameter with the first preset impact parameter and the second preset impact parameter; If the abnormal area impact parameter is less than or equal to the first preset impact parameter, the energy dispatch ratio of each abnormal area is adjusted to 0.98 times the corresponding initial energy dispatch ratio; If the abnormal area impact parameter is less than or equal to the second preset impact parameter and greater than the first preset impact parameter, the energy dispatch ratio of each abnormal area is adjusted to 0.94 times the corresponding initial energy dispatch ratio; If the abnormal area impact parameter is greater than the second preset impact parameter, the energy dispatch ratio of each abnormal area is adjusted to 0.91 times the corresponding initial energy dispatch ratio; The first preset influence parameter is 0.5, and the second preset influence parameter is 0.8.

[0050] Specifically, after completing the adjustment of the energy scheduling ratio, the analysis layer re-determines whether the operating parameters of the power supply layer are qualified based on the average consumption ratio, and if the operating parameters of the power supply layer are still determined to be abnormal, the expected average power consumption of the next analysis cycle is adjusted to the corresponding value based on the change in power consumption.

[0051] Specifically, the analysis layer is used to adjust the expected average power consumption of the next analysis cycle to a corresponding value based on the power consumption change, wherein: Based on the actual average power consumption obtained in each historical analysis period, an actual average power consumption curve is drawn, and the slope of the curve in the current analysis period is determined as the power consumption change; The increase in the expected average electricity consumption in the next analysis period is positively correlated with the change in electricity consumption.

[0052] Specifically, the actual average power consumption is the average value of the actual power consumption of each power consumption area in a single analysis period.

[0053] In this embodiment, preferably, comparing the power consumption change with a first preset power consumption change and a second preset power consumption change; If the power consumption change is less than or equal to the first preset power consumption change, the expected average power consumption in the next analysis period is adjusted to 1.13 times the current expected average power consumption; If the power consumption change is less than or equal to the second preset power consumption change and greater than the first preset power consumption change, the expected average power consumption in the next analysis period is adjusted to 1.25 times the current expected average power consumption; If the power consumption change is greater than the second preset power consumption change, the expected average power consumption in the next analysis period is adjusted to 1.28 times the current expected average power consumption; The first preset power consumption change is 0.3kW, and the second preset power consumption change is 0.5kW.

[0054] Specifically, the expected average power consumption is adjusted by adjusting the expected power consumption of each power consumption area.

[0055] Specifically, the analysis layer marks abnormal areas and adjusts the energy dispatch ratio. Abnormal areas are areas with abnormal electricity consumption and adjusts the energy dispatch ratio. The energy overflow parameter represents electricity demand that exceeds expectations. The abnormal area impact parameter reflects the degree of impact of the abnormal area on overall energy allocation. Promptly identifying and addressing abnormal electricity consumption areas prevents abnormal electricity consumption in individual areas from affecting the rationality of overall energy allocation and ensures that the electricity needs of all electricity-consuming areas are met. This resolves the issue of abnormal electricity consumption in individual areas, improves the fairness and rationality of energy allocation, and ensures normal electricity consumption in all electricity-consuming areas. The expected average electricity consumption is adjusted based on the change in electricity consumption, which reflects the changing trend of electricity demand. By analyzing the slope of the historical actual average electricity consumption curve, future changes in electricity demand are predicted and adjusted accordingly. The change in electricity consumption reflects the growth trend of electricity demand. Adjusting the expected average electricity consumption based on the changing trend of electricity demand allows energy allocation to better adapt to future electricity demand and avoid energy shortages or waste. This enables energy allocation to keep pace with changes in electricity demand, improving the adaptability and stability of energy supply.

[0056] Specifically, the analysis layer is used to determine whether to modify the power limit of each power consumption area based on the energy consumption parameter and the actual power consumption under the condition that the adjustment for the expected average power consumption is completed, wherein: Solve the ratio of the electric energy consumed by the storage layer in a single analysis period to the total electric energy of the storage layer at the beginning of the single analysis period to obtain the energy consumption parameter; The reduction in the power limit of each power consumption area is positively correlated with the energy consumption parameters.

[0057] In this embodiment, optionally, comparing the energy consumption parameter with a first preset consumption comparison value and a second preset consumption comparison value; If the energy consumption parameter is less than or equal to the first preset consumption comparison value, the power limit of each power consumption zone is adjusted to 0.92 times the initial power limit; If the energy consumption parameter is less than or equal to the second preset consumption comparison value and greater than the first preset consumption comparison value, the power limit of each power consumption zone is adjusted to 0.85 times the initial power limit; If the energy consumption parameter is greater than the second preset consumption comparison value, the power limit of each power consumption zone is adjusted to 0.71 times the initial power limit; The first preset consumption ratio value is 0.5, and the second preset consumption ratio value is 0.8.

[0058] Specifically, the analysis layer is used to adjust the power limit of the application area to a corresponding value based on the actual power consumption, wherein: The increase in power limit is negatively correlated with actual power consumption.

[0059] In this embodiment, optionally, Comparing the actual power consumption with the first preset power consumption comparison value and the second preset power consumption comparison value; If the actual power consumption is less than or equal to the first preset power consumption comparison value, the power limit of the power consumption area is adjusted to 1.08 times the current power limit; If the actual power consumption is less than or equal to the second preset power consumption comparison value and greater than the first preset power consumption comparison value, the power limit of the power consumption area is adjusted to 1.04 times the current power limit; If the actual power consumption is greater than the second preset power consumption comparison value, the power limit of the corresponding power consumption area is adjusted to 1.02 times the current power limit; The first preset power consumption comparison value is 1.11Qi, and the second preset power consumption comparison value is 1.23Qi, where Qi is the expected power consumption of the corresponding power consumption area.

[0060] Specifically, the analysis layer adjusts the power limits for each power consumption area based on energy consumption parameters and actual power consumption. Energy consumption parameters reflect the energy consumption of the energy storage layer. When energy consumption parameters are high, the energy reserves of the energy storage layer are insufficient. In this case, the power limits for each power consumption area are reduced to reduce energy consumption. When energy reserves in the energy storage layer are insufficient, energy consumption is controlled by adjusting the power limits to ensure a sustainable energy supply. Furthermore, power limits are adjusted based on actual power consumption to avoid energy waste or power shortages caused by excessively high or low power limits. When energy reserves are insufficient, energy consumption is controlled by adjusting the power limits, ensuring a sustainable energy supply and improving energy efficiency. The power limits for each building are adjusted based on the actual energy usage of each power consumption area. Reasonable adjustment of the power limits for each power consumption area ensures a sustainable energy supply and optimizes energy management.

[0061] Thus far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection of the present invention.

[0062] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that the present invention is susceptible to various modifications and variations. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A distributed energy scheduling method enabled by edge computing of the Internet of Things, characterized in that: include: S1, collecting actual electricity consumption information of each electricity consumption area; S2, periodically determining an average consumption ratio based on the actual power consumption and expected power consumption of each power consumption area; S3, determining the expected power consumption of each power consumption area based on the actual power consumption of each power consumption area in each analysis period, and the energy dispatch ratio for each power consumption area, and distributing and dispatching power to each power consumption area; S4, determining whether the operating parameters of the power supply layer are qualified based on the average consumption ratio, including: Determine whether the operating parameters of the power supply layer are qualified and continue to use the current power supply scheduling data for energy scheduling, or, when it is determined that the operating parameters of the power supply layer are abnormal, adjusting the power supply dispatch data based on the consumption difference, including identifying the abnormal area based on the actual power consumption and correcting the energy dispatch ratio for the abnormal area based on the abnormal area impact parameter and the energy overflow parameter, or correcting the expected average power consumption; When the adjustment of the energy scheduling ratio is completed, the operating parameters of the power supply layer are re-determined to be qualified based on the average consumption ratio. When the operating parameters of the power supply layer are still determined to be abnormal, the expected average power consumption of the next analysis cycle is adjusted to the corresponding value based on the change in power consumption.

2. The distributed energy scheduling method enabled by edge computing of the Internet of Things according to claim 1 is characterized in that: The process of periodically determining whether the operating parameters of the power supply layer are qualified based on the average consumption ratio includes: It is used to calculate the ratio of actual power consumption to expected average power consumption of a single power consumption area, calculate the average power consumption ratio of each power consumption area, and obtain the average consumption ratio; The expected average electricity consumption is the average of the expected electricity consumption in each electricity consumption area; If the average consumption ratio is less than or equal to the preset average consumption ratio, the operating parameters of the power supply layer are determined to be qualified, and the current power supply scheduling data is continuously used for energy scheduling; If the average consumption ratio is greater than the preset average consumption ratio, it is determined that the operating parameters of the power supply layer are abnormal, and the power supply scheduling data is adjusted based on the consumption difference.

3. The distributed energy scheduling method enabled by edge computing of the Internet of Things according to claim 2 is characterized in that: Adjust power supply scheduling data based on consumption differences, including: The variance of each calculated power ratio is used to determine the consumption difference; If the consumption difference is less than or equal to the preset consumption difference, the expected average power consumption of the next analysis period is adjusted to the corresponding value based on the power consumption change; If the consumption difference is greater than the preset consumption difference, the abnormal area is identified based on the actual power consumption, and the energy scheduling ratio for the abnormal area is corrected based on the abnormal area impact parameter and the energy overflow parameter.

4. The distributed energy scheduling method enabled by edge computing of the Internet of Things according to claim 3 is characterized in that: The process of identifying abnormal areas based on actual power consumption includes: Mark the power consumption area where the ratio of actual power consumption to the corresponding expected power consumption is greater than the preset predicted ratio as an abnormal area; The process of correcting the energy dispatch ratio for a single abnormal area based on the energy overflow parameter includes: The energy spillover parameter is the difference between the actual electricity consumption of a single electricity consumption area and the expected average electricity consumption of all electricity consumption areas; The increase in the energy dispatch ratio of a single abnormal area is positively correlated with the energy spillover parameter.

5. The distributed energy scheduling method enabled by edge computing of the Internet of Things according to claim 4 is characterized in that: The process of correcting the energy dispatch ratio for each abnormal area based on the abnormal area impact parameters includes: The ratio of the number of statistically abnormal areas to the total number of power consumption areas is determined as the abnormal area impact parameter; The reduction in the energy dispatch ratio for abnormal areas is positively correlated with the impact parameters of abnormal areas.

6. The distributed energy scheduling method enabled by edge computing of the Internet of Things according to claim 5 is characterized in that: When the adjustment of the energy scheduling ratio is completed, the operating parameters of the power supply layer are re-determined to be qualified based on the average consumption ratio. When the operating parameters of the power supply layer are still determined to be abnormal, the expected average power consumption of the next analysis cycle is adjusted to the corresponding value based on the change in power consumption.

7. The distributed energy scheduling method enabled by edge computing of the Internet of Things according to claim 6 is characterized in that: The process of adjusting the expected average power consumption in the next analysis period to the corresponding value based on the power consumption change includes: Based on the actual average power consumption obtained in each historical analysis period, an actual average power consumption curve is drawn, and the slope of the curve in the current analysis period is determined as the power consumption change; The increase in the expected average electricity consumption in the next analysis period is positively correlated with the change in electricity consumption.

8. The distributed energy scheduling method enabled by edge computing of the Internet of Things according to claim 7 is characterized in that: When the adjustment for the expected average power consumption is completed, it is determined whether to modify the power limit of each power consumption area based on the energy consumption parameter and the actual power consumption, wherein: Solve the ratio of the electric energy consumed by the storage layer for storing electric energy in a single analysis cycle to the total electric energy of the storage layer at the beginning of the single analysis cycle to obtain the energy consumption parameter; The reduction in the power limit of each power consumption area is positively correlated with the energy consumption parameters.

9. The distributed energy scheduling method enabled by edge computing of the Internet of Things according to claim 8 is characterized in that: Based on the actual power consumption, the power limit of the power application area is adjusted to the corresponding value, where: The increase in power limit is negatively correlated with actual power consumption.

Images