A distributed energy scheduling method empowered by internet of things edge computing

Abstract

The present application relates to the technical field of energy scheduling, and particularly relates to a distributed energy scheduling method based on Internet of Things edge computing, actual power consumption information of each power consumption area is collected; an average consumption ratio is determined based on actual power consumption and expected power consumption of each power consumption area; expected power consumption of each power consumption area is determined based on actual power consumption of each power consumption area in each analysis period, and electric energy is distributed and scheduled to each power consumption area according to the energy scheduling proportion of each power consumption area; whether the operation parameter of the power supply layer is qualified is determined based on the average consumption ratio, when it is determined that the operation parameter of the power supply layer is abnormal, the power supply scheduling data is adjusted based on the consumption difference, including identifying an abnormal area based on the actual power consumption and correcting the energy scheduling proportion for the abnormal area based on the abnormal area influence parameter and the energy overflow parameter, or correcting the expected average power consumption. The present application realizes accurate prediction of expected power consumption and accurate determination of energy scheduling data.

Description

Technical Field

[0001] This invention relates to the field of energy data processing technology, and in particular to a distributed energy dispatching method enabled by IoT edge computing. Background Technology

[0002] With the continued growth of global energy demand and increasing attention to sustainable energy use, distributed energy systems, as an efficient and flexible energy supply method, are gradually becoming a development trend in the future energy sector. Distributed energy systems disperse power generation equipment near users, such as solar photovoltaic panels, wind turbines, and small gas turbines, enabling on-site energy production and utilization, reducing transmission losses, and improving energy efficiency. However, the widespread application of distributed energy systems has also brought a series of challenges, among which energy dispatching is particularly prominent.

[0003] Distributed energy systems involve a vast amount of energy data, such as the actual electricity consumption of each region, the power output of generating equipment, and the capacity of energy storage layers. This data is characterized by diversity, real-time nature, and massive volume. Electricity consumption behaviors and demands vary across different regions. Therefore, effectively processing and analyzing this complex data is fundamental to achieving precise energy dispatch.

[0004] Through IoT technology, real-time data collection from various electricity-consuming areas and power generation equipment can be achieved. Smart meters are installed in each area to record actual electricity consumption in real time, and the data is transmitted to a data center via wireless communication technology. The data center stores and manages this massive amount of data for subsequent analysis and processing.

[0005] Energy dispatching in distributed energy systems requires consideration of multiple factors, such as electricity demand, generation capacity, and energy storage status. Because these factors are inherently uncertain, accurate forecasting is crucial for optimizing energy dispatching. Data forecasting helps dispatchers understand in advance the electricity demand and generation status of various areas, thereby enabling them to rationally plan energy production and distribution.

[0006] Chinese Patent Publication No. CN111600304B discloses a building power dispatching method, device, and equipment. The method includes classifying first typical load curves of typical electrical equipment to obtain second typical load curves under different power consumption modes. A mathematical model is then established based on the second typical load curves, improving the accuracy of the description of building users' power load. An adaptive dynamic programming method is then used to calculate the power dispatching strategy for each type of typical electrical equipment under different power consumption modes. Finally, the total dispatching data for typical electrical equipment is calculated based on the first power dispatching data and a first preset weight vector under different power consumption modes. However, the above technical solution has the following problems: it does not consider the real-time collection and analysis of the power usage of each building, resulting in low accuracy of the predicted expected power consumption and affecting energy utilization efficiency. Summary of the Invention

[0007] To address this issue, the present invention provides a distributed energy dispatching method enabled by IoT edge computing, which overcomes the problem in the prior art that does not take into account the real-time collection and analysis of the power usage of each building, resulting in low accuracy of the predicted power consumption and affecting energy utilization efficiency.

[0008] To achieve the above objectives, the present invention provides a distributed energy dispatching method enabled by IoT edge computing, comprising:

[0009] S1 collects actual electricity consumption information for each electricity consumption area;

[0010] S2, periodically determines the average consumption ratio based on the actual electricity consumption and expected electricity consumption of each electricity consumption area;

[0011] S3, based on the actual electricity consumption of each electricity consumption area in each analysis period, determine the expected electricity consumption of each electricity consumption area, and the energy dispatch ratio for each electricity consumption area, and allocate and dispatch electricity to each electricity consumption area.

[0012] S4, determine whether the operating parameters of the power supply layer are qualified based on the average consumption ratio, including...

[0013] The operating parameters of the power supply layer are deemed acceptable, and the current power supply dispatch data is continuously used for energy dispatching.

[0014] Alternatively, when the operating parameters of the power supply layer are determined to be abnormal, the power supply scheduling data can be adjusted based on the consumption difference, including identifying abnormal areas based on actual power consumption and correcting the energy scheduling ratio for abnormal areas based on the impact parameters and energy overflow parameters of abnormal areas, or correcting the expected average power consumption.

[0015] When adjusting the energy dispatch ratio, the operating parameters of the power supply layer are re-determined based on the average consumption ratio to determine whether they are qualified. If the operating parameters of the power supply layer are still determined to be abnormal, the expected average power consumption for the next analysis period is adjusted to the corresponding value based on the change in power consumption.

[0016] Furthermore, the process of periodically determining whether the operating parameters of the power supply layer are qualified based on the average consumption ratio includes:

[0017] It is used to calculate the ratio of actual electricity consumption to expected average electricity consumption in a single electricity consumption area, calculate the average ratio of electricity consumption in each electricity consumption area, and obtain the average consumption ratio.

[0018] The expected average electricity consumption is the average of the expected electricity consumption in each electricity consumption area;

[0019] 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 deemed qualified, and the current power supply scheduling data is continuously used for energy scheduling.

[0020] If the average consumption ratio is greater than the preset average consumption ratio, the operating parameters of the power supply layer are determined to be abnormal, and the power supply scheduling data is adjusted based on the consumption difference.

[0021] Furthermore, power supply scheduling data is adjusted based on consumption differences, including:

[0022] This is used to determine the variance of each calculated electricity ratio as the consumption difference.

[0023] If the consumption difference is less than or equal to the preset consumption difference, the expected average electricity consumption for the next analysis period will be adjusted to the corresponding value based on the change in electricity consumption.

[0024] If the consumption difference is greater than the preset consumption difference, abnormal areas are identified based on the actual electricity consumption, and the energy dispatch ratio for the abnormal areas is adjusted based on the abnormal area impact parameters and energy overflow parameters.

[0025] Furthermore, the process of identifying abnormal areas based on actual electricity consumption includes:

[0026] Areas where the ratio of actual electricity consumption to corresponding expected electricity consumption is greater than the preset predicted percentage are marked as abnormal areas.

[0027] The process of adjusting the energy dispatch ratio for a single anomalous region based on energy spillover parameters includes:

[0028] 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;

[0029] The increase in the energy dispatch ratio of a single anomalous region is positively correlated with the energy spillover parameter.

[0030] Furthermore, the process of adjusting the energy dispatch ratio for each anomalous region based on the impact parameters of the anomalous region includes:

[0031] The ratio of the number of abnormal areas to the total number of all electricity consumption areas is determined as the abnormal area impact parameter.

[0032] The reduction in the proportion of energy dispatch for anomalous areas is positively correlated with the impact parameters of anomalous areas.

[0033] Furthermore, when adjusting the energy dispatch ratio, the operating parameters of the power supply layer are re-determined based on the average consumption ratio to determine whether they are qualified. If the operating parameters of the power supply layer are still determined to be abnormal, the expected average power consumption for the next analysis period is adjusted to the corresponding value based on the change in power consumption.

[0034] Furthermore, the process of adjusting the expected average electricity consumption for the next analysis period to the corresponding value based on the change in electricity consumption includes:

[0035] Based on the actual average electricity consumption obtained in each historical analysis period, the actual average electricity consumption curve is plotted, and the slope of the curve in the current analysis period is determined as the change in electricity consumption.

[0036] The expected increase in average electricity consumption for the next analysis period is positively correlated with the change in electricity consumption.

[0037] Furthermore, when adjusting for the expected average electricity consumption, it is determined whether to adjust the power limit for each electricity consumption area based on energy consumption parameters and actual electricity consumption.

[0038] The energy consumption parameter is obtained by calculating the ratio of the energy consumed by the energy storage layer in a single analysis cycle to the total energy of the energy storage layer at the beginning of the single analysis cycle.

[0039] The reduction in the power restriction limits in each electricity consumption area is positively correlated with the energy consumption parameters.

[0040] Furthermore, based on actual electricity consumption, the power restriction limit for the application area will be adjusted to a corresponding value, wherein,

[0041] The increase in the power limit is negatively correlated with the actual electricity consumption.

[0042] Compared with existing technologies, the advantages of this invention lie in its comprehensive consideration of multiple data indicators, including not only single electricity consumption data but also average consumption ratio, consumption difference, abnormal area impact parameters, energy overflow parameters, and power consumption changes. By using multi-dimensional data, changes and characteristics of electricity consumption are determined from different perspectives. Through comprehensive analysis of this multi-dimensional data, a more complete understanding of the changing trends in electricity demand is achieved, thereby improving the accuracy of expected electricity consumption forecasts. Within each analysis cycle, subsequent expected electricity consumption is adjusted based on the comparison between actual and expected electricity consumption. When abnormal operating parameters of the power supply layer are determined, corresponding scheduling data adjustments are made based on different data indicators, including correcting energy dispatch ratios and adjusting expected average electricity consumption. Through dynamic adjustment and feedback, timely responses to changes in electricity consumption are provided, making the expected electricity consumption forecast more consistent with the actual situation, thereby improving forecast accuracy. The identification and handling of abnormal areas, as well as countermeasures for various abnormal situations, enhance the robustness of expected electricity consumption forecasts. When an abnormal area is identified where the ratio of actual electricity consumption to corresponding expected electricity consumption is greater than the preset predicted ratio, the energy dispatch ratio for the abnormal area is adjusted based on the abnormal area impact parameters and energy overflow parameters. This targeted analysis and processing of the abnormal situation further improves the accuracy of the prediction data.

[0043] Furthermore, by improving the accuracy of expected electricity consumption forecasts, more precise future electricity demand data for each electricity-consuming area can be provided, resulting in more accurate energy delivery data and improved energy distribution efficiency across different areas. Accurate forecasting of expected electricity consumption enables rational planning of energy reserves. Determining appropriate energy reserve levels based on actual needs reduces energy reserve costs while simultaneously improving overall energy utilization efficiency.

[0044] 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 characterizes the overall matching degree between the actual electricity consumption and the expected average electricity 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 can meet the electricity 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. Timely detection of problems in the operation of the power supply layer avoids energy waste or insufficient power supply caused by unreasonable energy allocation, thus improving the efficiency of energy management.

[0045] Furthermore, power dispatch data is adjusted based on consumption difference, which reflects the dispersion between the actual electricity consumption and the expected average electricity consumption in each power consumption area. When the average consumption ratio is less than or equal to the preset average consumption ratio, the consumption difference is small, and the electricity consumption in each power consumption area is relatively stable. The expected average electricity consumption is adjusted to adapt to the overall electricity consumption trend. When the average consumption ratio is less than or equal to the preset average consumption ratio, the consumption difference is large. At this time, due to the existence of abnormal electricity consumption areas, the deviation between the actual electricity consumption and the expected electricity consumption in each power consumption area varies greatly. By adopting different adjustment strategies for different electricity consumption situations, the flexibility and accuracy of energy dispatch are improved, energy allocation is optimized, and energy utilization efficiency is further improved.

[0046] Furthermore, the analysis layer marks abnormal regions and adjusts the energy dispatch ratio; abnormal regions are those with abnormal electricity consumption. Energy overflow parameters characterize electricity demand exceeding expectations. Abnormal region impact parameters reflect the degree of impact of abnormal regions on overall energy allocation. Timely detection and handling of abnormal electricity consumption regions prevent the impact of abnormal electricity consumption in individual regions on the rationality of overall energy allocation, ensuring that the electricity demand of each region is met. Solving the problem of abnormal electricity consumption in individual regions improves the fairness and rationality of energy allocation and ensures normal electricity use in each region. The expected average electricity consumption is adjusted based on changes 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, thereby adjusting the expected average electricity consumption. Changes in electricity consumption reflect the growth trend of electricity demand. Adjusting the expected average electricity consumption according to the changing trend of electricity demand allows energy allocation to better adapt to future electricity demand, avoiding energy shortages or waste. This enables energy allocation to keep up with changes in electricity demand in a timely manner, improving the adaptability and stability of energy supply.

[0047] Furthermore, the analysis layer adjusts the power limitation of each power consumption area based on energy consumption parameters and actual electricity consumption. The energy consumption parameters reflect the energy consumption of the energy storage layer. When the energy consumption parameters are high, the energy reserves of the energy storage layer are insufficient. In this case, the power limitation of each power consumption area is reduced to decrease energy consumption. When the energy reserves of the energy storage layer are insufficient, energy consumption is controlled by adjusting the power limitation to ensure a sustainable energy supply. Simultaneously, the power limitation is adjusted according to actual electricity consumption to avoid energy waste or insufficient power supply caused by excessively high or low power limitations. In the case of insufficient energy reserves, adjusting the power limitation to control energy consumption ensures a sustainable energy supply and improves energy utilization efficiency. The power limitation of each building is adjusted based on the actual energy consumption of each power consumption area. Reasonable adjustment of the power limitation of each power consumption area achieves a sustainable energy supply and optimizes energy management. Attached Figure Description

[0048] Figure 1 This is a block diagram of a distributed energy dispatching system enabled by IoT edge computing, as described in an embodiment of the present invention.

[0049] Figure 2 This is a flowchart illustrating the steps of a distributed energy scheduling method empowered by IoT edge computing, as described in an embodiment of the present invention. Detailed Implementation

[0050] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0051] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0052] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0053] Please see Figure 1 The diagram shown is a block diagram of a distributed energy dispatching system enabled by IoT edge computing according to an embodiment of the present invention. The distributed energy dispatching system enabled by IoT edge computing according to the present invention includes:

[0054] The power generation layer includes several power generation devices;

[0055] An energy storage layer, which is connected to the power generation layer, includes several energy storage devices for storing electrical energy.

[0056] The power consumption layer is connected to the energy storage layer and the power generation layer respectively, and includes several power consumption areas, each of which includes several power consumption terminals;

[0057] The data acquisition layer, which is connected to the power consumption layer, includes several power acquisition devices respectively set in each power consumption area for periodically acquiring the actual power consumption of each power consumption area.

[0058] The power supply layer is connected to the power generation layer, the energy storage layer, the power consumption layer and the acquisition layer respectively. It is used to allocate and schedule the power of the energy storage layer and the power generation layer, so as 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.

[0059] The analysis layer, which is connected to the acquisition layer and the power supply layer respectively, is used to determine whether the operating parameters of the power supply layer are qualified based on the average consumption ratio, and when the operating parameters of the power supply layer are determined to be 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 abnormal areas.

[0060] Please see Figure 2 The diagram shown is a flowchart illustrating the steps of a distributed energy scheduling method empowered by IoT edge computing according to an embodiment of the present invention. The distributed energy scheduling method empowered by IoT edge computing according to the present invention includes:

[0061] S1 collects actual electricity consumption information for each electricity consumption area;

[0062] S2, periodically determines the average consumption ratio based on the actual electricity consumption and expected electricity consumption of each electricity consumption area;

[0063] S3, based on the actual electricity consumption of each electricity consumption area in each analysis period, determine the expected electricity consumption of each electricity consumption area, and the energy dispatch ratio for each electricity consumption area, and allocate and dispatch electricity to each electricity consumption area.

[0064] S4, determine whether the operating parameters of the power supply layer are qualified based on the average consumption ratio, including...

[0065] When the operating parameters of the power supply layer are determined to be abnormal, the power supply scheduling data is adjusted 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.

[0066] Alternatively, the operating parameters of the power supply layer may be deemed acceptable, and the current power supply scheduling data may be continuously used for energy scheduling.

[0067] Specifically, there are no restrictions on the specific structure of the power generation equipment, which may include rooftop photovoltaics and building gas turbine units, as these are existing technologies and will not be elaborated further.

[0068] Specifically, the specific structure of the power acquisition device is not limited, and it may include a voltmeter and an ammeter.

[0069] Specifically, there is no limitation on the specific method for predicting the expected electricity consumption of 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. The actual electricity consumption in different periods is assigned corresponding weights, with the weight of recent data being greater than that of long-term data. This is existing technology and will not be elaborated further.

[0070] Specifically, to determine the energy dispatch ratio for each electricity consumption area, energy can be allocated according to the proportion of the expected electricity consumption of each electricity consumption area to the total expected electricity consumption of all electricity consumption areas, which will not be elaborated further.

[0071] Specifically, the power supply layer determines the expected electricity consumption of each power consumption area based on its actual electricity consumption within each analysis period. The power supply layer then determines the energy dispatch ratio for each power consumption area by combining the power generation capacity of the generation layer, the energy storage capacity of the energy storage layer, and the expected electricity consumption of each power consumption area. This ratio represents the relative share of electricity obtained by each power consumption area from the generation layer and the energy storage layer. Based on the expected electricity consumption and the energy dispatch ratio of each power consumption area, the power supply layer sends instructions to the power generation equipment in the generation layer to adjust the power generation capacity. If the expected electricity consumption increases, the power supply layer will require the power generation equipment to increase its power generation capacity; conversely, it will reduce the power generation capacity if the expected electricity consumption decreases.

[0072] Specifically, determining the expected electricity consumption provides a basis for adjusting the power generation capacity of the power generation equipment in the power generation layer. By accurately predicting the electricity consumption of each region, the power generation layer can rationally arrange its power generation plan, avoiding energy waste due to over-generation or power shortages due to under-generation. Determining the expected electricity consumption also enables the power supply layer to rationally manage the energy storage layer's capacity. Based on the expected electricity consumption, the power supply layer determines the charging and discharging strategy of the energy storage layer, ensuring that the energy storage layer has sufficient capacity to release during peak electricity consumption periods and can promptly store excess energy during off-peak periods.

[0073] Specifically, by determining the energy dispatch ratio, the resource allocation of the power generation and energy storage layers is optimized. Based on the electricity demand of each power-consuming area, the energy dispatch ratio is adjusted to ensure the effective utilization of electrical resources.

[0074] Specifically, actual electricity consumption characterizes the energy consumption of each power-consuming area within a specific time period. The power supply layer determines the expected electricity consumption and energy dispatch ratio. Based on historical electricity consumption data and current trends, it predicts the future electricity demand of each power-consuming area to rationally allocate energy between the power generation and energy storage layers. Expected electricity consumption is a forecast of future electricity consumption for each power-consuming area, and the energy dispatch ratio determines the share of each area in the total energy allocation. Advance planning of energy allocation avoids energy shortages or waste, ensuring that the electricity demand of each power-consuming area is met. This achieves rational energy allocation, improves the stability and reliability of energy supply, and reduces energy waste.

[0075] It should be noted 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 between the actual electricity consumption and the expected average electricity consumption in each electricity consumption area within historical data. It can be a value near the average or median of the ratios 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 proportion, the higher the accuracy of electricity consumption prediction. The method described in this invention can determine the setting of each preset parameter by selecting the value with the highest proportion based on the data distribution as the preset standard parameter, using weighted summation to use the obtained value as the preset standard parameter, or other selection methods, as long as the method described in this invention can clearly define different specific situations in the single-item judgment process through the obtained values.

[0076] 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:

[0077] It is used to calculate the ratio of actual electricity consumption to expected average electricity consumption in a single electricity consumption area, and to calculate the average value of each electricity consumption ratio to obtain the average consumption ratio;

[0078] The expected average electricity consumption is the average of the expected electricity consumption in each electricity consumption area;

[0079] 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 deemed qualified, and the current power supply scheduling data is continuously used for energy scheduling.

[0080] If the average consumption ratio is greater than the preset average consumption ratio, the operating parameters of the power supply layer are determined to be abnormal, and the power supply scheduling data is adjusted based on the consumption difference.

[0081] Specifically, the preset average consumption ratio is selected within the range [1, 1.05].

[0082] The analysis layer periodically determines whether the operating parameters of the power supply layer are up to standard based on the average consumption ratio. The average consumption ratio characterizes the overall matching degree between the actual electricity consumption and the expected average electricity 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 can meet the electricity 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. Timely detection of problems in the operation of the power supply layer avoids energy waste or insufficient power supply caused by unreasonable energy allocation, thus improving the efficiency of energy management.

[0083] Specifically, the analysis layer is used to adjust power supply scheduling data based on consumption differences, including:

[0084] This is used to determine the variance of each calculated electricity ratio as the consumption difference.

[0085] If the consumption difference is less than or equal to the preset consumption difference, the expected average electricity consumption for the next analysis period will be adjusted to the corresponding value based on the change in electricity consumption.

[0086] If the consumption difference is greater than the preset consumption difference, abnormal areas are identified based on the actual electricity consumption, and the energy dispatch ratio for the abnormal areas is adjusted based on the abnormal area impact parameters and energy overflow parameters.

[0087] Specifically, the preset consumption difference is selected within the range [0.1, 0.16].

[0088] Specifically, the ratio of the amount of electricity planned to be transmitted from the energy storage layer to a single power consumption area within a preset analysis period to the total amount of electricity in the energy storage layer is determined as the energy dispatch ratio for a single power consumption area.

[0089] Specifically, power dispatch data is adjusted based on consumption difference, which reflects the dispersion between the actual electricity consumption and the expected average electricity consumption in each power consumption area. When the average consumption ratio is less than or equal to the preset average consumption ratio, the consumption difference is small, and the electricity consumption in each power consumption area is relatively stable. The expected average electricity consumption is adjusted to adapt to the overall electricity consumption trend. When the average consumption ratio is greater than the preset average consumption ratio, the consumption difference is large. At this time, due to the existence of abnormal electricity consumption areas, the deviation between the actual and expected electricity consumption in each power consumption area varies greatly. Different adjustment strategies are adopted for different electricity consumption situations, which improves the flexibility and accuracy of energy dispatch, optimizes energy allocation, and further improves energy utilization efficiency.

[0090] Specifically, the analysis layer is used to mark areas where the ratio of actual electricity consumption to corresponding expected electricity consumption is greater than a preset predicted proportion as abnormal areas;

[0091] The analysis layer is used to adjust the energy dispatch ratio for a single abnormal region based on energy spillover parameters, wherein...

[0092] 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;

[0093] The increase in the energy dispatch ratio of a single anomalous region is positively correlated with the energy spillover parameter.

[0094] In this embodiment, optionally,

[0095] The energy overflow parameter is compared with the first preset overflow parameter and the second preset overflow parameter;

[0096] If the energy overflow parameter is less than or equal to the first preset overflow parameter, the energy scheduling ratio of a single abnormal region will be adjusted to 1.12 times the corresponding initial energy scheduling ratio.

[0097] 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 scheduling ratio of a single abnormal region will be adjusted to 1.21 times the corresponding initial energy scheduling ratio.

[0098] If the energy overflow parameter is greater than the second preset overflow parameter, the energy dispatch ratio of a single abnormal region will be adjusted to 1.31 times the corresponding initial energy dispatch ratio.

[0099] The first preset overflow parameter is set to 1.15, and the second preset overflow parameter is set to 1.25.

[0100] Specifically, the preset prediction ratio is 1.

[0101] Specifically, the analysis layer is used to adjust the energy dispatch ratio for each abnormal region based on the impact parameters of the abnormal region;

[0102] The analysis layer is used to determine the ratio of the number of abnormal areas to the total number of electricity consumption areas as the abnormal area influence parameter.

[0103] The reduction in the proportion of energy dispatch for anomalous areas is positively correlated with the impact parameters of anomalous areas.

[0104] In this embodiment, optionally,

[0105] Compare the impact parameters of the abnormal area with the first preset impact parameters and the second preset impact parameters;

[0106] If the impact parameter of the abnormal area is less than or equal to the first preset impact parameter, the energy dispatch ratio of each abnormal area will be adjusted to 0.98 times the corresponding initial energy dispatch ratio.

[0107] If the impact parameter of the abnormal area 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 will be adjusted to 0.94 times the corresponding initial energy dispatch ratio.

[0108] If the impact parameter of the abnormal area is greater than the second preset impact parameter, the energy dispatch ratio of each abnormal area will be adjusted to 0.91 times the corresponding initial energy dispatch ratio.

[0109] The first preset influence parameter is set to 0.5, and the second preset influence parameter is set to 0.8.

[0110] Specifically, after completing the adjustment of the energy dispatch ratio, the analysis layer re-determines whether the operating parameters of the power supply layer are qualified based on the average consumption ratio. If the operating parameters of the power supply layer are still determined to be abnormal, the expected average power consumption for the next analysis cycle is adjusted to the corresponding value based on the power consumption change.

[0111] Specifically, the analysis layer is used to adjust the expected average power consumption for the next analysis period to a corresponding value based on the change in power consumption, wherein,

[0112] Based on the actual average electricity consumption obtained in each historical analysis period, the actual average electricity consumption curve is plotted, and the slope of the curve in the current analysis period is determined as the change in electricity consumption.

[0113] The expected increase in average electricity consumption for the next analysis period is positively correlated with the change in electricity consumption.

[0114] Specifically, the actual average electricity consumption is the average of the actual electricity consumption of each electricity consumption area within a single analysis period.

[0115] In this embodiment, preferably,

[0116] Compare the change in power consumption with the first preset change in power consumption and the second preset change in power consumption;

[0117] If the change in power consumption is less than or equal to the first preset change in power consumption, the expected average power consumption for the next analysis period will be adjusted to 1.13 times the current expected average power consumption.

[0118] If the change in power consumption is less than or equal to the second preset change in power consumption and greater than the first preset change in power consumption, the expected average power consumption for the next analysis period will be adjusted to 1.25 times the current expected average power consumption.

[0119] If the change in power consumption is greater than the second preset change in power consumption, the expected average power consumption for the next analysis period will be adjusted to 1.28 times the current expected average power consumption.

[0120] The first preset power consumption change is 0.3kW, and the second preset power consumption change is 0.5kW.

[0121] Specifically, the expected average electricity consumption is adjusted by adjusting the expected electricity consumption in each electricity consumption area.

[0122] Specifically, the analysis layer identifies and corrects energy dispatch ratios for abnormal regions, which are defined as areas with abnormal electricity consumption. Energy overflow parameters characterize electricity demand exceeding expectations. Abnormal region impact parameters reflect the degree of influence of these regions on overall energy allocation. Timely detection and handling of abnormal electricity consumption areas prevents the impact of individual areas' abnormal electricity consumption on the overall rationality of energy allocation, ensuring that the electricity needs of all areas are met. Solving the problem of abnormal electricity consumption in individual areas improves the fairness and rationality of energy allocation, ensuring normal electricity use in all areas. The expected average electricity consumption is adjusted based on changes in electricity consumption, which reflects the changing trend of electricity demand. By analyzing the slope of historical actual average electricity consumption curves, future changes in electricity demand are predicted, thereby adjusting the expected average electricity consumption. Changes in electricity consumption reflect the growth trend of electricity demand. Adjusting the expected average electricity consumption according to the changing trend of electricity demand allows energy allocation to better adapt to future electricity demand, avoiding energy shortages or waste. This enables energy allocation to keep pace with changes in electricity demand, improving the adaptability and stability of energy supply.

[0123] Specifically, the analysis layer is used to determine whether to adjust the power limits for each electricity consumption area based on energy consumption parameters and actual electricity consumption, provided that adjustments for the expected average electricity consumption have been completed.

[0124] The energy consumption parameter is obtained by calculating the ratio of the electrical energy consumed by the energy storage layer in a single analysis cycle to the total electrical energy of the energy storage layer at the beginning of the single analysis cycle.

[0125] The reduction in the power restriction limits in each electricity consumption area is positively correlated with the energy consumption parameters.

[0126] In this embodiment, optionally,

[0127] Compare the energy consumption parameters with the first preset consumption comparison value and the second preset consumption comparison value;

[0128] If the energy consumption parameter is less than or equal to the first preset consumption comparison value, the power limit of each power consumption area will be adjusted to 0.92 times the initial power limit.

[0129] 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 area will be adjusted to 0.85 times the initial power limit.

[0130] If the energy consumption parameter is greater than the second preset consumption comparison value, the power limit of each power consumption area will be adjusted to 0.71 times the initial power limit.

[0131] The first preset consumption comparison value is 0.5, and the second preset consumption comparison value is 0.8.

[0132] Specifically, the analysis layer is used to adjust the power restriction of the application area to a corresponding value based on the actual power consumption, wherein,

[0133] The increase in the power limit is negatively correlated with the actual electricity consumption.

[0134] In this embodiment, optionally,

[0135] Compare the actual electricity consumption with the first preset electricity consumption comparison value and the second preset electricity consumption comparison value;

[0136] If the actual power consumption is less than or equal to the first preset power consumption comparison value, the power restriction limit for the power application area will be adjusted to 1.08 times the current power restriction limit.

[0137] If the actual electricity consumption is less than or equal to the second preset electricity consumption ratio and greater than the first preset electricity consumption ratio, the power restriction of the electricity application area will be adjusted to 1.04 times the current power restriction.

[0138] If the actual electricity consumption is greater than the second preset electricity consumption comparison value, the power limit for the application area will be adjusted to 1.02 times the current power limit.

[0139] The first preset electricity consumption comparison value is 1.11Qi, and the second preset electricity consumption comparison value is 1.23Qi, where Qi is the expected electricity consumption of the corresponding electricity consumption area.

[0140] Specifically, the analysis layer adjusts the power restriction limits for each electricity consumption area based on energy consumption parameters and actual electricity 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 restriction limits for each electricity consumption area are reduced to decrease energy consumption. When the energy reserves of the energy storage layer are insufficient, energy consumption is controlled by adjusting the power restriction limits to ensure a sustainable energy supply. Simultaneously, the power restriction limits are adjusted according to actual electricity consumption to avoid energy waste or insufficient power supply caused by excessively high or low power restrictions. In the case of insufficient energy reserves, adjusting the power restriction limits to control energy consumption ensures a sustainable energy supply and improves energy utilization efficiency. The power restriction limits for each building are adjusted based on the actual energy consumption of each electricity consumption area. Reasonable adjustment of the power restriction limits for each electricity consumption area achieves a sustainable energy supply and optimizes energy management.

[0141] The technical solution of the present invention has been described above with reference to 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 can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0142] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for distributed energy scheduling enabled by Internet of Things (IoT) edge computing, characterized in that, Comprise: S1, collect the actual power consumption information of each power consumption area; S2, periodically determine the average consumption ratio based on the actual power consumption and the expected power consumption of each power consumption area; S3, 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 allocate and schedule power to each power consumption area according to the energy scheduling ratio of each power consumption area; S4, determine whether the operation parameters of the power supply layer are qualified based on the average consumption ratio, including, determine that the operation 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 operation parameters of the power supply layer are abnormal, adjust the power supply scheduling data based on the consumption difference, including identifying abnormal areas based on the actual power consumption and correcting the energy scheduling ratio for the abnormal areas based on the abnormal area influence parameter and the energy overflow parameter, or correcting the expected average power consumption; when the adjustment of the energy scheduling ratio is completed, re-determine whether the operation parameters of the power supply layer are qualified according to the average consumption ratio, and when it is still determined that the operation parameters of the power supply layer are abnormal, adjust the expected average power consumption of the next analysis period to the corresponding value based on the power consumption change; the process of identifying abnormal areas based on the actual power consumption, comprising: mark the power consumption area whose ratio of actual power consumption to corresponding expected power consumption is greater than the preset prediction proportion as an abnormal area; the process of correcting the energy scheduling ratio for a single abnormal area based on the energy overflow parameter, comprising: the energy overflow parameter is the difference between the actual power consumption of a single power consumption area and the expected average power consumption of each power consumption area; the increase range of the energy scheduling ratio of a single abnormal area is positively correlated with the energy overflow parameter; the process of correcting the energy scheduling ratio for each abnormal area based on the abnormal area influence parameter, comprising: determine the ratio of the number of abnormal areas to the total number of power consumption areas as the abnormal area influence parameter; the decrease range of the energy scheduling ratio for abnormal areas is positively correlated with the abnormal area influence parameter.

2. The IoT edge computing empowered distributed energy scheduling method according to claim 1, wherein, the process of periodically determining whether the operation parameters of the power supply layer are qualified based on the average consumption ratio, comprising: calculate the power consumption ratio of the actual power consumption of a single power consumption area to the expected average power consumption, calculate the average of the power consumption ratios of each power consumption area, and obtain the average consumption ratio; the expected average power consumption is the average of the expected power consumptions of each power consumption area; if the average consumption ratio is less than or equal to the preset average consumption ratio, it is determined that the operation parameters of the power supply layer are 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 operation parameters of the power supply layer are abnormal, and the power supply scheduling data is adjusted based on the consumption difference.

3. The IoT edge computing empowered distributed energy scheduling method of claim 2, wherein, adjust the power supply scheduling data based on the consumption difference, comprising: calculate the variance of each power consumption ratio to determine the consumption difference; if the consumption difference is less than or equal to the preset consumption difference, adjust the expected average power consumption of the next analysis period to the corresponding value based on the power consumption change; if the consumption difference is greater than the preset consumption difference, identify abnormal areas based on the actual power consumption, and correct the energy scheduling ratio for the abnormal areas based on the abnormal area influence parameter and the energy overflow parameter.

4. The IoT edge computing empowered distributed energy scheduling method of claim 3, wherein, When the adjustment for the energy scheduling ratio is completed, it is determined again whether the operation parameters of the power supply layer are qualified according to the average consumption ratio, and when it is still determined that the operation parameters of the power supply layer are abnormal, the expected average power consumption of the next analysis period is adjusted to a corresponding value based on the power consumption change amount.

5. The IoT edge computing empowered distributed energy scheduling method of claim 4, wherein, The expected average power consumption of the next analysis period is adjusted to a corresponding value based on the power consumption change amount, wherein, An actual average power consumption curve is drawn based on the obtained actual average power consumption in each historical analysis period, and a slope of the curve at the current analysis period is determined as the power consumption change amount; The increase range of the expected average power consumption of the next analysis period is positively correlated with the power consumption change amount.

6. The IoT edge computing empowered distributed energy scheduling method of claim 5, wherein, When the adjustment for the expected average power consumption is completed, it is determined whether to correct the limit power of each power consumption area based on the energy consumption parameter and the actual power consumption, wherein, The energy consumption parameter is obtained by solving a ratio of the electric energy consumed by the power storage layer for storing electric energy in a single analysis period to the total electric energy of the power storage layer at the beginning of the single analysis period; The decrease range of the limit power of each power consumption area is positively correlated with the energy consumption parameter.

7. The IoT edge computing empowered distributed energy scheduling method of claim 6, wherein, The limit power of the power consumption area is adjusted to a corresponding value based on the actual power consumption, wherein, The increase range of the limit power is negatively correlated with the actual power consumption.

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