Method for controlling orderly power utilization of large industrial user cluster under peak load

Abstract

The application discloses a method for controlling orderly power consumption of a large industrial user cluster under sharp peak load, relates to the technical field of large industrial power consumption control, and aims at solving the problem of inaccurate processing of equipment power consumption data under sharp peak load in an industrial area.The application can further improve the power consumption control scheme of the sharp peak load equipment by first evaluating the power consumption data of the equipment under sharp peak load, can further improve the effect of power consumption control in the industrial area by optimizing the parameters of the equipment through the power consumption control scheme, and can quickly obtain the target power consumption feature corresponding to each key feature in the key feature subset through the identification model of each load amount, and then accurately determine the operation feature of the load amount, thereby improving the efficiency and accuracy of the sharp peak load data acquisition under the industry.

Description

Technical Field

[0001] This invention relates to the field of power control technology for large industrial users, specifically a method for orderly power control of large industrial user clusters under peak load conditions. Background Technology

[0002] Large-scale industrial power consumption control mainly involves managing and reducing power consumption through a series of measures and technical means in order to achieve the goals of energy conservation, emission reduction, and reduced operating costs.

[0003] Chinese patent CN104348150B discloses a power load management method, server, terminal, and system. It mainly acquires equipment information, power consumption information, and load management instructions from electrical devices to generate power control instructions, which are then sent to the power load management terminal. This allows the terminal to adjust the power of each electrical device according to the instructions. This achieves precise load management of the power supply lines, solving the problem of peak loads in the power system during peak electricity consumption periods. However, while this patent solves the power control problem, the following issues remain in practical operation: 1. The lack of targeted power supply plans for equipment with different load ranges resulted in unsatisfactory power control performance.

[0004] 2. Peak load data was not analyzed for its characteristics, which made it impossible to implement targeted power control based on the specific power consumption characteristics of the equipment. Summary of the Invention

[0005] The purpose of this invention is to provide a method for orderly power consumption control of large industrial user clusters under peak load. By first evaluating the power consumption data of equipment under peak load, the power consumption control scheme of the equipment under peak load can be further improved. Then, by optimizing the parameters of the equipment through the power consumption control scheme, the effect of power consumption control in the industrial area can be further improved. By constructing an identification model for each load, the target power consumption characteristics corresponding to the target time series data of each key feature in the key feature subset can be quickly obtained, thereby accurately determining the operating characteristics of the load. This improves the efficiency and accuracy of peak load data acquisition in industry and solves the problems in the prior art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: A method for orderly power consumption control of large industrial user clusters under peak load conditions includes the following steps: S1: Collect electricity consumption data from large industrial users in real time and generate charts from the collected electricity consumption data; S2: Based on the generated electricity consumption data chart, acquire the electricity operation characteristic data under peak load; S3: Based on the acquired power consumption operation characteristic data, evaluate and optimize the electrical equipment corresponding to each power consumption operation characteristic data.

[0007] Preferably, the real-time collection of power consumption data in S1 includes: The power consumption of each piece of equipment in the industrial park that requires electricity is monitored and data is collected in real time through sensors and smart meters. Acquire real-time data on electricity consumption, match each collected data point with the electrical equipment that consumes the data, and then match the electrical equipment with the specific location of the industrial zone to which the equipment belongs. Once the electricity consumption data is matched with the electrical equipment and the location of the electrical equipment in the industrial area, a unique code is assigned.

[0008] Preferably, the generation of charts for the electricity consumption data in S1 includes: Confirm the unique code label data; Once the unique code label data is confirmed, retrieve the associated data from the unique code label data; The associated data includes: electricity consumption data, electrical equipment, the location of the industrial zone to which the equipment belongs, and electricity consumption data collection data; The unique coded label data for each time period and the associated data corresponding to that unique coded label data are used to generate fluctuation curve data. After the fluctuation curve data is generated, the highest and lowest curves are highlighted.

[0009] Preferably, the generation of charts for the electricity consumption data in S1 further includes: Extract the unique code label of the highlighted curve and the associated data corresponding to the unique code label data separately; The highest and lowest curves and their corresponding data are extracted separately and used to generate charts. The highest curve data and its corresponding data in the generated charts are then labeled as peak load electricity consumption data.

[0010] Preferably, the acquisition of electricity consumption operation characteristic data in S2 includes: The peak load power consumption data is redundant and dimensionally reduced to obtain the processed industrial power data. Feature extraction is performed on industrial power data, and an initial feature set of industrial power data is obtained based on the extraction results. Key features related to peak load electricity consumption data are retrieved from the initial feature set and integrated into a subset of key features; Obtain the topology information and preset operation mode information of industrial power supply, as well as the area to which each device in the industrial zone belongs and the node attributes of the power supply node; The power topology weight value of each device's power node is determined based on the region to which each device belongs, the node attributes and topology information of the power node, and the preset operating mode information.

[0011] Preferably, the acquisition of electricity consumption operation characteristic data in S2 further includes: The basic value of each load in the industrial power data is calculated based on the power topology weight value of each device's power consumption node; Obtain the time-series feature information corresponding to industrial power data, wherein the time-series feature information is retrieved from the database; Extract time-series data of each load from industrial power data based on time-series characteristic information; Determine the electricity consumption characteristics of each load based on the time series data of each load; The time-series data and baseline values ​​of each load are used as input samples for the model, while the electricity consumption characteristics of each load are used as output samples to train the preset network model to obtain the identification model for each load.

[0012] Preferably, the acquisition of electricity consumption operation characteristic data in S2 further includes: The target electricity consumption characteristics corresponding to the target time series data of each key feature in the key feature subset are obtained by using the identification model of each load; Based on the target electricity consumption characteristics of each key feature, obtain the first operating characteristic of each load; Obtain the changes in the target electricity consumption characteristics of each load in the power data, and determine the electricity consumption change rules for each load based on the changes. Loads with a similarity to electricity consumption change rules greater than or equal to a preset threshold are identified as the same type of load, and the second target operating characteristic of any load in each type of load is identified as the final operating characteristic of the same type of load. The final operating characteristics will be the power consumption operating characteristics data of peak load power consumption data.

[0013] Preferably, the evaluation of the electricity consumption operation characteristic data in S3 includes: Electricity consumption operation characteristic data is acquired, and then clustering is performed on the acquired electricity consumption operation characteristic data; After clustering the electricity consumption operation characteristic data, we obtain the power operation data and voltage operation data of the corresponding equipment. The power operation data and voltage operation data of the corresponding equipment are converted into numerical values; The converted values ​​are used to generate curves, ultimately yielding power operation curve data and voltage operation curve data.

[0014] Preferably, the evaluation of the electricity consumption operation characteristic data in S3 also includes: Power operation curve data and voltage operation curve data are imported into the electricity consumption assessment model for model building; The constructed model is compared with the standard power consumption model of the device; After comparing the models, the comparison model data is obtained, and the values ​​of the comparison model data are confirmed. The power control range level of the equipment is determined based on the data from the comparative model. Among them, the power control range level is divided into Class I control range, Class II control range and Class III control range.

[0015] Preferably, the optimization of the power consumption operation characteristic data in S3 includes: The power control range level of the equipment is obtained from the electrical operation characteristic data; Different equipment should be optimized according to different power control range levels; First, the equipment data in the power control range level is acquired, and different power control schemes are formulated based on different equipment data. The power control schemes for different equipment in each level are different. Power control solutions can be obtained from a solution database or customized manually.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. The method for orderly power consumption control of large industrial user clusters under peak load provided by the present invention can quickly obtain the target power consumption characteristics corresponding to the target time series data of each key feature in the key feature subset by constructing an identification model for each load, thereby accurately determining the operating characteristics of the load. This improves the efficiency and accuracy of peak load data acquisition in industry. By classifying the loads, the operating characteristics of the same type of load can be uniformly statistically analyzed, eliminating the need to perform operating characteristic statistics for individual loads. This improves the efficiency of acquiring peak load power consumption data of different equipment in different time periods.

[0017] 2. The method for orderly power consumption control of large industrial user clusters under peak load provided by the present invention first evaluates the power consumption data of the equipment under peak load, which can further improve the power consumption control scheme of the equipment under peak load. Then, by optimizing the parameters of the equipment through the power consumption control scheme, the effect of power consumption control in the industrial area can be further improved. Attached Figure Description

[0018] Figure 1This is a schematic diagram of the overall steps of the present invention; Figure 2 This is a schematic diagram of the overall operation method of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] To address the issue of inaccurate electricity consumption data collection in existing technologies, where multiple identical devices exist within industrial areas and the data collection process fails to effectively differentiate between them, thus resulting in low accuracy of the collected data, please refer to [the relevant documentation]. Figure 1 and Figure 2 This embodiment provides the following technical solution: A method for orderly power consumption control of large industrial user clusters under peak load conditions includes the following steps: S1: Collect electricity consumption data from large industrial users in real time and generate charts from the collected electricity consumption data; Among them, charts can highlight key information such as peak, valley, and trend of electricity consumption in different time periods for different devices; S2: Based on the generated electricity consumption data chart, acquire the electricity operation characteristic data under peak load; Among them, by constructing an identification model for each load, the target electricity consumption characteristics corresponding to the target time series data of each key feature in the key feature subset can be quickly obtained, thereby accurately determining the operating characteristics of the load and improving the efficiency and accuracy of peak load data acquisition in industry. S3: Based on the acquired power consumption operation characteristic data, evaluate and optimize the electrical equipment corresponding to each power consumption operation characteristic data; The differences in electricity consumption are mapped to different levels of electricity control based on the numerical differences. Different levels correspond to different levels of control in the electricity control scheme, thereby improving the effectiveness of electricity control for equipment in the industrial zone.

[0021] Real-time collection of power consumption data in S1 includes: The power consumption of each piece of equipment in the industrial park that requires electricity is monitored and data is collected in real time through sensors and smart meters. Acquire real-time data on electricity consumption, match each collected data point with the electrical equipment that consumes the data, and then match the electrical equipment with the specific location of the industrial zone to which the equipment belongs. Once the electricity consumption data is matched with the electrical equipment and the location of the electrical equipment in the industrial area, a unique code is assigned.

[0022] Specifically, the system first collects power consumption data from electrical equipment in the industrial area using sensors and smart meters. After collecting the power consumption data, the electrical equipment and the area to which it belongs are identified and uniquely coded, thereby further improving the accuracy of acquiring power consumption data for each electrical device.

[0023] Chart generation for electricity consumption data in S1 includes: Confirm the unique code label data; Once the unique code label data is confirmed, retrieve the associated data from the unique code label data; The associated data includes: electricity consumption data, electrical equipment, the location of the industrial zone to which the equipment belongs, and electricity consumption data collection data; The unique coded label data for each time period and the associated data corresponding to that unique coded label data are used to generate fluctuation curve data. After the fluctuation curve data is generated, the highest and lowest curves are highlighted.

[0024] Extract the unique code label of the highlighted curve and the associated data corresponding to the unique code label data separately; The highest and lowest curves and their corresponding data are extracted separately and used to generate charts. The highest curve data and its corresponding data in the generated charts are then labeled as peak load electricity consumption data.

[0025] Specifically, based on the unique coded labels obtained, fluctuation curve data is generated for the power consumption data of different electrical devices in each time period. The generated fluctuation curve data can clearly show the fluctuation of the data, making it easier to understand and analyze the data. At the same time, it can also more quickly obtain the peak load power consumption of different devices in different time periods. Then, the highest curve data in the generated fluctuation curve data is used to generate a chart. The chart can highlight key information such as the peak value, valley value, and trend of power consumption of different devices in different time periods.

[0026] To address the problem in existing technologies where peak load electricity consumption data in industrial settings is acquired but not analyzed for data characteristics, thus preventing targeted electricity control based on specific equipment consumption characteristics, please refer to [link to relevant documentation]. Figure 1and Figure 2 This embodiment provides the following technical solution: The acquisition of power consumption operation characteristic data in S2 includes: The peak load power consumption data is redundant and dimensionally reduced to obtain the processed industrial power data. Feature extraction is performed on industrial power data, and an initial feature set of industrial power data is obtained based on the extraction results. Key features related to peak load electricity consumption data are retrieved from the initial feature set and integrated into a subset of key features; Obtain the topology information and preset operation mode information of industrial power supply, as well as the area to which each device in the industrial zone belongs and the node attributes of the power supply node; The power topology weight value of each device's power node is determined based on the region to which each device belongs, the node attributes and topology information of the power node, and the preset operating mode information.

[0027] The basic value of each load in the industrial power data is calculated based on the power topology weight value of each device's power consumption node; Obtain the time-series feature information corresponding to industrial power data, wherein the time-series feature information is retrieved from the database; Extract time-series data of each load from industrial power data based on time-series characteristic information; Determine the electricity consumption characteristics of each load based on the time series data of each load; The time-series data and baseline values ​​of each load are used as input samples for the model, while the electricity consumption characteristics of each load are used as output samples to train the preset network model to obtain the identification model for each load.

[0028] The target electricity consumption characteristics corresponding to the target time series data of each key feature in the key feature subset are obtained by using the identification model of each load; Based on the target electricity consumption characteristics of each key feature, obtain the first operating characteristic of each load; Obtain the changes in the target electricity consumption characteristics of each load in the power data, and determine the electricity consumption change rules for each load based on the changes. Loads with a similarity to electricity consumption change rules greater than or equal to a preset threshold are identified as the same type of load, and the second target operating characteristic of any load in each type of load is identified as the final operating characteristic of the same type of load. The final operating characteristics will be the power consumption operating characteristics data of peak load power consumption data.

[0029] Specifically, the peak load electricity consumption data is first subjected to redundancy and dimensionality reduction processing. This simplifies the peak load electricity consumption data into a smaller set, thereby reducing the complexity and dimensionality of the data. Furthermore, the most important features can be extracted from the peak load electricity consumption data, enabling the algorithm to predict results more accurately. By obtaining the initial feature set of industrial power data, feature normalization processing can be quickly performed on the data, allowing for the statistical analysis of data features in each dimension. This lays the foundation for subsequent determination of load operation characteristics. By constructing an identification model for each load, the target electricity consumption characteristics corresponding to the target time series data of each key feature in the key feature subset can be quickly obtained, thus accurately determining the operation characteristics of the load. This improves the efficiency and accuracy of peak load data acquisition in industry. By classifying the loads, the operation characteristics of similar loads can be uniformly statistically analyzed, eliminating the need for statistical analysis of individual loads and improving the efficiency of acquiring peak load electricity consumption data for different equipment at different time periods.

[0030] To address the problem in existing technologies where different devices and load ranges vary under peak loads, and where targeted power management schemes are not developed for devices within different load ranges, resulting in unsatisfactory power control performance, please refer to [link to relevant documentation]. Figure 1 and Figure 2 This embodiment provides the following technical solution: The evaluation of the power consumption operation characteristic data in S3 includes: Electricity consumption operation characteristic data is acquired, and then clustering is performed on the acquired electricity consumption operation characteristic data; After clustering the electricity consumption operation characteristic data, we obtain the power operation data and voltage operation data of the corresponding equipment. The power operation data and voltage operation data of the corresponding equipment are converted into numerical values; The converted values ​​are used to generate curves, ultimately yielding power operation curve data and voltage operation curve data.

[0031] Power operation curve data and voltage operation curve data are imported into the electricity consumption assessment model for model building; The constructed model is compared with the standard power consumption model of the device; After comparing the models, the comparison model data is obtained, and the values ​​of the comparison model data are confirmed. The power control range level of the equipment is determined based on the data from the comparative model. Among them, the power control range level is divided into Class I control range, Class II control range and Class III control range.

[0032] Optimization of power consumption operation characteristic data in S3 includes: The power control range level of the equipment is obtained from the electrical operation characteristic data; Different equipment should be optimized according to different power control range levels; First, the equipment data in the power control range level is acquired, and different power control schemes are formulated based on different equipment data. The power control schemes for different equipment in each level are different. Power control solutions can be obtained from a solution database or customized manually.

[0033] Specifically, the electrical operation characteristic data is first clustered. Data clustering facilitates data mining and information extraction, can be applied to large-scale datasets, and has good scalability. The constructed model is then compared with the standard power consumption model of the equipment. Based on the model comparison, the difference between the actual power consumption of the equipment and the standard power consumption can be obtained more quickly. At the same time, the difference in power consumption is mapped to the power control range level according to the value of the difference. Different levels correspond to different control levels of the power control scheme, thereby improving the effect of power control of equipment in the industrial area. By first evaluating the power consumption data of the equipment under peak load, the power control scheme of the equipment under peak load can be further improved. Then, by optimizing the parameters of the equipment through the power control scheme, the effect of power control in the industrial area can be further improved.

[0034] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0035] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for orderly power consumption control of large industrial user clusters under peak load conditions, characterized in that, Includes the following steps: S1: Collect electricity consumption data from large industrial users in real time and generate charts from the collected electricity consumption data; S2: Based on the generated electricity consumption data chart, acquire the electricity operation characteristic data under peak load; S3: Based on the acquired power consumption operation characteristic data, evaluate and optimize the electrical equipment corresponding to each power consumption operation characteristic data; The acquisition of electricity consumption operation characteristic data in S2 also includes: The target electricity consumption characteristics corresponding to the target time series data of each key feature in the key feature subset are obtained by using the identification model of each load; Based on the target electricity consumption characteristics of each key feature, obtain the first operating characteristic of each load; Obtain the changes in the target electricity consumption characteristics of each load in the power data, and determine the electricity consumption change rules for each load based on the changes. Loads with a similarity to the electricity consumption change rules greater than or equal to a preset threshold are identified as the same type of load, and the second operating characteristic of any load in each type of load is identified as the final operating characteristic of the same type of load. The final operating characteristics will be the electricity consumption operating characteristic data of peak load electricity consumption data; The evaluation of the power consumption operation characteristic data in S3 includes: Electricity consumption operation characteristic data is acquired, and then clustering is performed on the acquired electricity consumption operation characteristic data; After clustering the electricity consumption operation characteristic data, we obtain the power operation data and voltage operation data of the corresponding equipment. The power operation data and voltage operation data of the corresponding equipment are converted into numerical values; The converted values ​​are used to generate curves, ultimately yielding power operation curve data and voltage operation curve data.

2. The method for orderly power consumption control of large industrial user clusters under peak load as described in claim 1, characterized in that: Real-time collection of power consumption data in S1 includes: The power consumption of each piece of equipment in the industrial park that requires electricity is monitored and data is collected in real time through sensors and smart meters. Acquire real-time data on electricity consumption, match each collected data point with the electrical equipment that consumes the data, and then match the electrical equipment with the specific location of the industrial zone to which the equipment belongs. Once the electricity consumption data is matched with the electrical equipment and the location of the electrical equipment in the industrial area, a unique code is assigned.

3. The method for orderly power consumption control of large industrial user clusters under peak load as described in claim 2, characterized in that: Chart generation for electricity consumption data in S1 includes: Confirm the unique code label data; Once the unique code label data is confirmed, retrieve the associated data from the unique code label data; The associated data includes: electricity consumption data, electrical equipment, the location of the industrial zone to which the equipment belongs, and electricity consumption data collection data; The unique coded label data for each time period and the associated data corresponding to that unique coded label data are used to generate fluctuation curve data. After the fluctuation curve data is generated, the highest and lowest curves are highlighted.

4. The method for orderly power consumption control of large industrial user clusters under peak load as described in claim 3, characterized in that: The generation of charts for electricity consumption data in S1 also includes: Extract the unique code label of the highlighted curve and the associated data corresponding to the unique code label data separately; The highest and lowest curves and their corresponding data are extracted separately and used to generate charts. The highest curve data and its corresponding data in the generated charts are then labeled as peak load electricity consumption data.

5. The method for orderly power consumption control of large industrial user clusters under peak load as described in claim 4, characterized in that: The acquisition of power consumption operation characteristic data in S2 includes: The peak load power consumption data is redundant and dimensionally reduced to obtain the processed industrial power data. Feature extraction is performed on industrial power data, and an initial feature set of industrial power data is obtained based on the extraction results. Key features related to peak load electricity consumption data are retrieved from the initial feature set and integrated into a subset of key features; Obtain the topology information and preset operation mode information of industrial power supply, as well as the area to which each device in the industrial zone belongs and the node attributes of the power supply node; The power topology weight value of each device's power node is determined based on the region to which each device belongs, the node attributes and topology information of the power node, and the preset operating mode information.

6. The method for orderly power consumption control of large industrial user clusters under peak load as described in claim 5, characterized in that: The acquisition of electricity consumption operation characteristic data in S2 also includes: The basic value of each load in the industrial power data is calculated based on the power topology weight value of each device's power consumption node; Obtain the time-series feature information corresponding to industrial power data, wherein the time-series feature information is retrieved from the database; Extract time-series data of each load from industrial power data based on time-series characteristic information; Determine the electricity consumption characteristics of each load based on the time series data of each load; The time-series data and baseline values ​​of each load are used as input samples for the model, while the electricity consumption characteristics of each load are used as output samples to train the preset network model to obtain the identification model for each load.

7. The method for orderly power consumption control of large industrial user clusters under peak load as described in claim 6, characterized in that: The evaluation of electricity consumption operation characteristic data in S3 also includes: Power operation curve data and voltage operation curve data are imported into the electricity consumption assessment model for model building; The constructed model is compared with the standard power consumption model of the device; After comparing the models, the comparison model data is obtained, and the values ​​of the comparison model data are confirmed. The power control range level of the equipment is determined based on the data from the comparative model. Among them, the power control range level is divided into Class I control range, Class II control range and Class III control range.

8. The method for orderly power consumption control of large industrial user clusters under peak load as described in claim 7, characterized in that: Optimization of power consumption operation characteristic data in S3 includes: The power control range level of the equipment is obtained from the electrical operation characteristic data; Different equipment should be optimized according to different power control range levels; First, the equipment data in the power control range level is acquired, and different power control schemes are formulated based on different equipment data. The power control schemes for different equipment in each level are different. Power control solutions can be obtained from a solution database or customized manually.

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