Power load regulation system and method, electronic equipment and storage medium

Abstract

The invention discloses a power load adjusting system and method, electronic equipment and a storage medium. The method comprises an abnormity monitoring device which is used for monitoring the operation state of a power grid and generating abnormity information under the condition that the operation state of the power grid is abnormal; the data acquisition device comprises a plurality of power load data acquisition units and is used for acquiring power load data from a plurality of data sources in a power grid, and the power load data comprises real-time power load data and historical power load data; and the dispatching control device is connected with the abnormity monitoring device and the data acquisition device and is used for performing prediction based on the power load data through a prediction model after receiving the abnormity information, obtaining a power load regulation scheme and regulating the power grid based on the power load regulation scheme, and the prediction model is constructed based on historical power load data. According to the invention, the technical problem of low accuracy of power load adjustment is solved.

Description

Technical Field

[0001] The present invention relates to the field of electric power, and in particular to an electric power load regulation system, method, electronic equipment and storage medium. Background Art

[0002] During peak electricity consumption periods, a surge in load in local areas may cause excessive pressure on the power supply and even trigger power outages, seriously affecting users' electricity experience and the overall efficiency of the power system. Therefore, the rational allocation of power resources is crucial and the power load needs to be adjusted.

[0003] However, the power load regulation method in the related art is difficult to accurately grasp the abnormal load situation, resulting in insufficient accuracy when performing power load regulation.

[0004] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0005] The embodiments of the present invention provide a power load regulation system, method, electronic device and storage medium to at least solve the technical problem of low accuracy of power load regulation.

[0006] According to one aspect of an embodiment of the present invention, there is provided a power load regulation system, comprising: an abnormality monitoring device, for monitoring the operating state of a power grid, and generating abnormality information when there is an abnormality in the operating state of the power grid; a data acquisition device, comprising a plurality of power load data acquisition units, for acquiring power load data from a plurality of data sources in the power grid, wherein the plurality of data sources include at least: an electric meter and a sensor, and the power load data include real-time power load data and historical power load data; a dispatching control device, respectively connected to the abnormality monitoring device and the data acquisition device, for acquiring the collected power load data from the data acquisition device based on the abnormal information after receiving the abnormal information, performing prediction based on the power load data through a prediction model to obtain a power load regulation plan, and regulating the power grid based on the power load regulation plan, wherein the prediction model is constructed based on historical power load data.

[0007] Furthermore, the dispatching and control device includes: a preprocessing unit, which is respectively connected to the abnormal monitoring device and the data acquisition device, and is used to obtain the real-time power load data from the data acquisition device after receiving the abnormal information, and perform preprocessing operations on the real-time power load data to obtain the data after the preprocessing operation; a model building unit, which is connected to the data acquisition device, and is used to obtain the historical power load data from the data acquisition device, and use the historical power load data to train a preset model to obtain a prediction model; a prediction unit, which is connected to the preprocessing unit and the model building unit, and is used to perform predictions through the prediction model based on the data after the preprocessing operation to obtain a power load regulation plan.

[0008] Furthermore, the preprocessing unit includes: a data cleaning module, which is used to identify and remove abnormal values ​​and missing values ​​in the real-time power load data to obtain the real-time power load data after the data cleaning operation; a feature extraction module, which is used to perform feature extraction operations on the real-time power load data after the data cleaning operation to obtain data after the preprocessing operation.

[0009] Furthermore, the power load regulation system also includes an adjustment reference device, which includes: a rule generation unit, connected to the data acquisition device, and used to generate a load change rule based on historical power load data; a data visualization unit, connected to the dispatching control device, and used to convert the load change rule into a visualization chart, wherein the visualization chart is used to adjust the power load regulation plan.

[0010] Furthermore, the rule generating unit includes: a determination module, used to determine multiple daily average adjustable loads based on historical power load data; a modeling module, connected to the determination module, used to model the changing trend of power load data according to multiple daily average adjustable loads to obtain the load change rule.

[0011] Furthermore, the power load regulation system also includes a data encryption device, which includes: an encryption unit, used to set the data acquisition device to an encryption state that prohibits the acquisition of power load data; a judgment unit, used to judge whether the access permission is a preset permission after the data acquisition device is set to an encryption state that prohibits the acquisition of power load data; a decryption unit, connected to the judgment unit, used to set the data acquisition device to a decryption state that allows the acquisition of power load data when the access permission is the preset permission.

[0012] According to another aspect of an embodiment of the present invention, there is also provided a method for regulating electric load, including: monitoring the operating status of a power grid, and generating abnormal information when the operating status of the power grid is abnormal; acquiring collected electric load data from a data acquisition device based on the abnormal information, wherein the electric load data includes real-time electric load data and historical electric load data; performing prediction based on the real-time electric load data through a prediction model to obtain an electric load regulation scheme, wherein the prediction model is constructed based on the historical electric load data; and regulating the power grid based on the electric load regulation scheme.

[0013] Furthermore, a prediction is performed based on the real-time power load data through a prediction model to obtain a power load regulation plan, including: inputting the real-time power load data into the prediction model, using the prediction model to predict the power load regulation plan to obtain a preliminary power load regulation plan; adjusting the preliminary power load regulation plan to obtain a power load regulation plan.

[0014] Furthermore, the preliminary power load regulation plan is adjusted to obtain a power load regulation plan, including: determining multiple daily average adjustable loads based on historical power load data; modeling the change trend of power load data according to multiple daily average adjustable loads to obtain a load change law; converting the load change law into a visual chart; and adjusting the preliminary power load regulation plan based on the visual chart to obtain a power load regulation plan.

[0015] Furthermore, the power load regulation method also includes: when obtaining the collected power load data from the data acquisition device, judging the access permission; if the access permission is a preset permission, the data acquisition device allows the acquisition of the power load data; if the access permission is not a preset permission, the data acquisition device prohibits the acquisition of the power load data.

[0016] According to another aspect of an embodiment of the present invention, there is also provided an electronic device, comprising: a memory storing an executable program; and a processor for running the program, wherein the above-mentioned power load regulation method is executed when the program is running.

[0017] One aspect of the present application provides a computer-readable storage medium, which includes a stored executable program, wherein when the executable program runs, the device where the storage medium is located is controlled to execute the above-mentioned power load regulation method.

[0018] One aspect of the present application provides a computer program product, including a computer program, which implements the above-mentioned power load regulation method when executed by a processor.

[0019] In an embodiment of the present invention, a power load regulation system includes: an abnormality monitoring device for monitoring the operation state of the power grid, and generating abnormal information when the operation state of the power grid is abnormal; a data acquisition device including a plurality of power load data acquisition units for collecting power load data from a plurality of data sources in the power grid, the power load data including real-time power load data and historical power load data; a dispatching control device connected to the abnormality monitoring device and the data acquisition device respectively, and used to predict based on the power load data through a prediction model after receiving the abnormal information, obtain a power load regulation scheme, and adjust the power grid based on the power load regulation scheme. It is easy to notice that the abnormality monitoring device is used to fully identify the abnormality of the operation state of the power grid. When there is an abnormality, the dispatching control device obtains the power load data from the data acquisition device based on the abnormal information obtained from the abnormality monitoring device, and the prediction model predicts the power load regulation scheme based on the power load data, thereby achieving the purpose of fully identifying the abnormal operation state of the power grid. When the power grid is in an abnormal operation state, the power load regulation scheme is predicted based on the power load data, thereby achieving the technical effect of improving the accuracy of power load regulation, thereby solving the technical problem of low accuracy of power load regulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0021] Figure 1 is a schematic diagram of a power load regulation system according to an embodiment of the present invention;

[0022] Figure 2 is a schematic diagram of an optional data acquisition device according to an embodiment of the present invention;

[0023] Figure 3 is a schematic diagram of an optional rule generating unit according to an embodiment of the present invention;

[0024] Figure 4 is a schematic diagram of an optional judgment unit according to an embodiment of the present invention;

[0025] Figure 5 is a flow chart of a power load regulation method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0026] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0027] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0028] Example 1

[0029] According to an embodiment of the present invention, a method embodiment of a power load regulation system is provided. Figure 1 is a schematic diagram of a power load regulation system according to an embodiment of the present invention. Figure 1 As shown, the method comprises the following steps:

[0030] The abnormality monitoring device 10 is used to monitor the operation status of the power grid and generate abnormality information when there is an abnormality in the operation status of the power grid.

[0031] The above-mentioned operation status of the power grid refers to the working status of the power grid at a certain moment or over a period of time, including but not limited to the generation, transmission, distribution and consumption of electricity, the levels of electrical parameters such as voltage, current, frequency, and the health status and operating efficiency of each device and component in the power grid. The power load status can be determined based on the operation status of the power grid. If there is an abnormality in the operation status of the power grid, it can be considered that there is an abnormality in the power load in the power grid, and the power load in the power grid needs to be adjusted.

[0032] The above abnormal information is used to indicate that there is abnormal power load in the power grid and the power load in the power grid needs to be adjusted, including but not limited to information such as abnormal type, occurrence time and location. The abnormal information can trigger the dispatching control device to obtain power load data and predict the operation of the power load adjustment plan.

[0033] In an optional embodiment, the abnormality monitoring device can collect the more critical grid parameters in the power grid, such as voltage, current, frequency, power factor, etc., and then set a series of thresholds to compare the collected grid parameters with the thresholds to determine whether there is an abnormality in the operation status of the power grid. Specifically, when the grid parameters deviate from the preset normal range, it is judged that there is an abnormality in the operation status of the power grid. For example, if the voltage of a certain grid node suddenly drops by more than 10%, or the frequency fluctuates by more than 0.2 Hz, it is determined that there is an abnormality in the node. After identifying the abnormal situation, the abnormality monitoring device will generate and send abnormal information to the dispatching control device.

[0034] The data acquisition device 12 includes multiple power load data acquisition units for collecting power load data from multiple data sources in the power grid, wherein the multiple data sources include at least: electricity meters and sensors, and the power load data includes real-time power load data and historical power load data.

[0035] The above-mentioned power load data is data describing the load characteristics in the power system, that is, data describing the power consumption characteristics, which may be the consumed electric energy or the consumed electric power, but is not limited thereto.

[0036] The above-mentioned real-time power load data refers to the power load data collected at the current moment or in the most recent time window, which can usually be collected in real time through smart meters, sensors and other devices, and can be used to predict power load regulation plans through prediction models.

[0037] The above-mentioned historical power load data is the power load data collected over a period of time in the past, which can be power load data on different time scales such as day, week, month or year, and can be used to train the prediction model.

[0038] The above-mentioned power load data collection unit is installed in the power grid and is used to collect power load data. Different power load data collection units can collect power load data from different data sources.

[0039] In an optional embodiment, Figure 2 is a schematic diagram of an optional data acquisition device according to an embodiment of the present invention, such as Figure 2As shown, the data collection device includes multiple power load data collection units: power load data collection unit 21, power load data collection unit 22, ... power load data collection unit 2n. Power load information can be collected from different data sources through different power load data collection units. The data source can include electric meters and sensors, or smart devices, or the way users report data. The collection process can be real-time or periodic. When the collection process is real-time collection, the collected data is stored, and the real-time collection to the device is used as real-time power load data, and the previously collected data is used as historical power load data. When the collection process is periodic collection, the data collected in the period closest to the current time is used as real-time power load data, and the data collected in the previous period is used as historical power load data.

[0040] When the data source is an electric meter, an electric load data collection unit for collecting electric load data from the electric meter is used to communicate with the electric meter and regularly read electric energy readings to obtain the electric load data. For example, the cycle for collecting electric energy readings can be half an hour or one hour, but is not limited to this.

[0041] When the data source is a sensor, the sensor can monitor parameters such as voltage and current at key nodes in the power grid. A power load data acquisition unit that collects power load data from the sensor is connected to the sensor through a network, receives data sent by the sensor in real time, and obtains the power load data after processing.

[0042] The dispatching control device 14 is connected to the abnormality monitoring device and the data acquisition device respectively, and is used for obtaining the collected power load data from the data acquisition device based on the abnormal information after receiving the abnormal information, making predictions based on the power load data through the prediction model to obtain the power load regulation plan, and regulating the power grid based on the power load regulation plan, wherein the prediction model is constructed based on historical power load data.

[0043] The above-mentioned prediction model is a model for predicting power load regulation solutions. It is built based on historical power load data. By analyzing the load change trend and periodicity in real-time power load data, it predicts the power demand for a period of time in the future and generates a power load regulation solution. A variety of algorithms can be applied to the prediction model, including but not limited to time series analysis, machine learning, etc.

[0044] The above-mentioned power load regulation scheme is a strategy formulated based on the predicted power demand in the future. The power load regulation scheme may include but is not limited to power generation adjustment, power distribution adjustment, energy storage scheduling, etc. By implementing the power load regulation scheme, we can better cope with load fluctuations, optimize power supply and consumption, and ensure the stable operation of the power grid and the efficient use of power resources.

[0045] In an optional embodiment, the dispatch control device obtains the accumulated historical power load data from the data acquisition device, and uses the historical power load data to train the prediction model, so that the prediction model has the ability to predict the power load adjustment plan based on the power load data. When the dispatch control device receives abnormal information, it obtains real-time power load data from the data acquisition device, inputs the real-time power load data into the prediction model, and uses the prediction model to predict the power load demand in the future period of time, as well as the power load adjustment plan that can meet the power load demand.

[0046] For example, if the prediction model predicts that a substation will face a risk of overload in the future, the prediction model can simultaneously generate an electric load regulation plan to increase power generation and adjust the load distribution of other substations. The specific adjustment values ​​of the increased power generation and the load distribution of other substations can be determined based on the actual prediction situation and are not limited here.

[0047] Then, the power grid is adjusted based on the power load regulation plan to ensure the balance between power supply and demand, so as to avoid power grid failures and improve the reliability of the power system.

[0048] In an embodiment of the present invention, a power load regulation system includes: an abnormality monitoring device for monitoring the operation state of the power grid, and generating abnormal information when the operation state of the power grid is abnormal; a data acquisition device including a plurality of power load data acquisition units for collecting power load data from a plurality of data sources in the power grid, the power load data including real-time power load data and historical power load data; a dispatching control device connected to the abnormality monitoring device and the data acquisition device respectively, and used to predict based on the power load data through a prediction model after receiving the abnormal information, obtain a power load regulation scheme, and adjust the power grid based on the power load regulation scheme. It is easy to notice that the abnormality monitoring device is used to fully identify the abnormality of the operation state of the power grid. When there is an abnormality, the dispatching control device obtains the power load data from the data acquisition device based on the abnormal information obtained from the abnormality monitoring device, and the prediction model predicts the power load regulation scheme based on the power load data, thereby achieving the purpose of fully identifying the abnormal operation state of the power grid. When the power grid is in an abnormal operation state, the power load regulation scheme is predicted based on the power load data, thereby achieving the technical effect of improving the accuracy of power load regulation, thereby solving the technical problem of low accuracy of power load regulation.

[0049] In one embodiment of the present application, the dispatching and control device includes: a preprocessing unit, which is respectively connected to the abnormal monitoring device and the data acquisition device, and is used to obtain real-time power load data from the data acquisition device after receiving abnormal information, and perform preprocessing operations on the real-time power load data to obtain data after the preprocessing operation; a model building unit, which is connected to the data acquisition device, and is used to obtain historical power load data from the data acquisition device, and use the historical power load data to train a preset model to obtain a prediction model; a prediction unit, which is connected to the preprocessing unit and the model building unit, and is used to perform predictions through the prediction model based on the data after the preprocessing operation to obtain a power load regulation plan.

[0050] The above-mentioned preprocessing operation is a step before using the prediction model for prediction. Before the real-time power load data is input into the prediction model, a series of processing is performed on the original real-time power load data to improve the quality of the data and thus improve the accuracy of the prediction. It may include but is not limited to data cleaning, data conversion, feature engineering, data standardization, normalization, etc.

[0051] In an optional embodiment, after receiving the abnormal information, the real-time power load data is obtained from the data acquisition device, and the real-time power load data can be cleaned to remove the abnormal values ​​in the real-time power load data, and then the real-time power load data is converted to convert non-numeric data, such as date and time, into a numerical format, unify the data format, and obtain the data after the preprocessing operation. Then the preprocessed data is input into the trained prediction model, and the prediction model will predict the future power load demand according to the rules learned during training, and generate a power load regulation plan. The content of the power load regulation plan may include when to start or shut down certain power generation units, power distribution control of power grid nodes, and which users to send demand response signals to reduce load, etc.

[0052] When training a prediction model, an initial model can be selected, which can be a time series prediction model, a deep learning model, or a machine learning model, but is not limited thereto. Then, historical power load data is obtained from a data acquisition device, and the sorted historical power load data is used to train the initial model, and the parameters of the initial model are adjusted so that the model can learn the laws and trends of load changes from the data, thereby obtaining a prediction model with prediction capabilities.

[0053] In one embodiment of the present application, the preprocessing unit includes: a data cleaning module, which is used to identify and remove abnormal values ​​and missing values ​​in real-time power load data to obtain real-time power load data after data cleaning operation; a feature extraction module, which is used to perform feature extraction operation on the real-time power load data after data cleaning operation to obtain data after preprocessing operation.

[0054] The above-mentioned abnormal value is an abnormally high or low power consumption value in the power load data. For example, the power consumption at a certain moment suddenly jumps to an extreme value. The abnormal value may be caused by a meter failure or a data recording error.

[0055] The missing values ​​mentioned above are blank values ​​in the power load data caused by temporary failure of the meter or sensor, interruption of data transmission or human error.

[0056] The above-mentioned feature extraction operation is an operation of identifying and extracting features from real-time power load data. The extracted features may include: time features, weather features, historical trend features, event features, etc., but are not limited thereto.

[0057] In an optional embodiment, the data cleaning module identifies outliers and missing values ​​in the real-time power load data. For outliers, statistical methods can be used to identify them. If the power consumption value fed back by a certain meter deviates significantly from the average consumption of the meter or the average consumption of the entire power grid, then this value can be marked as an outlier. For missing values, the blank power consumption value fed back by a certain meter can be marked as a missing value. Once the outliers and missing values ​​are identified, the data cleaning module removes the outliers and missing values ​​to obtain data after the data cleaning operation to ensure the accuracy of the real-time power load data.

[0058] The feature extraction module will perform feature extraction on the real-time power load data after the data cleaning operation. Since the fluctuation of power demand is usually related to time, the time feature can be extracted from the timestamp contained in the real-time power load data after the data cleaning operation. In addition, the feature extraction module can also calculate the difference between the average load and the maximum load in a day in units of days to construct the load change trend feature. However, the feature types that the feature extraction module can extract are not limited to this.

[0059] In one embodiment of the present application, the power load regulation system also includes an adjustment reference device, which includes: a rule generation unit, connected to the data acquisition device, and used to generate a load change rule based on historical power load data; a data visualization unit, connected to the dispatching control device, and used to convert the load change rule into a visualization chart, wherein the visualization chart is used to adjust the power load regulation plan.

[0060] The above-mentioned load variation law is a pattern or trend of the power load changing over time. The load variation law may be a periodic change, for example, the power load may reach a peak in the morning and evening, and the power load may decrease in the late night. It may also be a seasonal change. For example, in summer, the power load may increase due to increased use of air conditioning, and in winter, there may be a load peak due to increased heating demand, but the load variation law may not be limited to this.

[0061] The above-mentioned visualization chart can present the load variation law in the form of a chart. The types of visualization charts may include but are not limited to time series charts, bar charts, scatter plots, etc.

[0062] In an optional embodiment, Figure 3 is a schematic diagram of an optional rule generation unit according to an embodiment of the present invention, such as Figure 3 As shown, the rule generation unit includes multiple historical load modules, including: historical load module 31, historical load module 32, historical load module 33...historical load module 3n, the historical load module is used to obtain historical power load data in the data acquisition device, and different historical load modules are used to obtain historical power load data at different times of the day. After the rule generation unit obtains the historical power load data, it calculates the daily average adjustable load with the day as the statistical unit, and determines the load change rule based on the daily average adjustable load.

[0063] Determine the type of visualization chart. The type of visualization chart can be a time series chart, a bar chart, or a scatter chart, but is not limited to this. With the date as the horizontal axis and the power load as the vertical axis, add the load data involved in the load change law to the visualization chart, and mark the key data points in the visualization chart, such as load peak, trough value, and abnormal load, so as to complete the drawing of the visualization chart. In addition, the load transformation law can also be input into the data visualization software, and the data visualization software can generate a visualization chart. The visualization chart can intuitively show the load change law, help to realize the precise scheduling of power resources, and reasonably allocate limited power resources to various time periods to avoid the situation of power surplus or shortage in local areas.

[0064] In one embodiment of the present application, the rule generating unit includes: a determination module, used to determine multiple daily average adjustable loads based on historical power load data; a modeling module, connected to the determination module, used to model the changing trend of the power load data according to multiple daily average adjustable loads to obtain a load change rule.

[0065] In an optional embodiment, the adjustable load at each time point of each day in the historical period is determined based on historical power load data, and then the average value of the adjustable load at each time point of each day is taken with the day as the statistical unit to obtain the daily average adjustable load.

[0066] Then, based on the daily average adjustable load, the difference operation of time series data is used to model the change trend of power load data and obtain the load change law. The modeling formula is as follows:

[0067]

[0068] Where t is time, Y(t) is the load value at time t, c is a constant, i and j are parameters, φ(i) and θ(j) are coefficients, Y(ti) is the load value at time (ti), and ε(tj) and ε(t) are error terms.

[0069] In one embodiment of the present application, the power load regulation system also includes a data encryption device, which includes: an encryption unit, used to set the data acquisition device to an encryption state that prohibits the acquisition of power load data; a judgment unit, used to judge whether the access permission is a preset permission after setting the data acquisition device to an encryption state that prohibits the acquisition of power load data; a decryption unit, connected to the judgment unit, and used to set the data acquisition device to a decryption state that allows the acquisition of power load data when the access permission is the preset permission.

[0070] The above access rights are used to determine whether the user has the authority to obtain the power load data in the data acquisition device.

[0071] The above-mentioned preset permissions are pre-set permissions and can be set according to actual application conditions. No specific restrictions are made on the preset permissions here.

[0072] In an optional embodiment, an encryption unit may be used to encrypt the data acquisition device, so that the data acquisition device is in an encryption state that prohibits acquisition of power load data. Figure 4 is a schematic diagram of an optional judgment unit according to an embodiment of the present invention, such as Figure 4 As shown, the judgment unit includes an account password login module 41 and a verification code login module 42, and the access right control in the judgment unit can be realized by means of verification login. Specifically, the user terminal corresponding to the account password login module is a tablet computer, a mobile phone or a laptop computer. After the data acquisition device is set to an encrypted state that prohibits the acquisition of power load data, when the user inputs the account number and the correct password matching the account number, it is considered that the user's access right is the preset right, and the decryption unit decrypts the data acquisition device, so that the data acquisition device is in a decryption state that allows the acquisition of power load data. The user terminal corresponding to the verification code login module is a mobile phone. After the data acquisition device is set to an encrypted state that prohibits the acquisition of power load data, when the user inputs the account number and the correct verification code matching the account number, it is considered that the user's access right is the preset right, and the decryption unit decrypts the data acquisition device, so that the data acquisition device is in a decryption state that allows the acquisition of power load data.

[0073] The encryption device can improve the security of the power load regulation system and prevent the power load data from being leaked.

[0074] Example 2

[0075] According to an embodiment of the present invention, an embodiment of an electric load regulation method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0076] It should be noted that the power load regulation method can be applied to the power load regulation system in the above-mentioned embodiment 1. The specific implementation and application scenarios of the power load regulation method are the same as those in embodiment 1 and will not be elaborated here.

[0077] Figure 5 is a flow chart of a power load regulation method according to an embodiment of the present invention. Figure 5 As shown, the process of the power load regulation method includes:

[0078] Step S502, monitoring the operation status of the power grid, and generating abnormal information when the operation status of the power grid is abnormal.

[0079] In an optional embodiment, monitoring the operating status of the power grid usually relies on sensors, electric meters and monitoring equipment distributed in the network. The main parameters monitored include, but are not limited to, voltage, current, frequency, power, temperature and humidity. When monitoring the operating status of the power grid, data points corresponding to the above main parameters are continuously collected, and algorithms are used to analyze these data in real time to identify any abnormal conditions that deviate from the normal operating range. For example, sudden changes in voltage or frequency, abnormal fluctuations in current, abnormal increases in equipment temperature, increased power loss, etc., abnormal conditions can indicate that there is excessive power load pressure in the current power grid, and the power load data needs to be adjusted. Once an abnormality is identified, abnormal information will be generated immediately, and the abnormal information can be used as a basis for the need to adjust the power load.

[0080] Step S504, based on the abnormal information, acquiring the collected power load data from the data collection device, wherein the power load data includes real-time power load data and historical power load data.

[0081] In an optional embodiment, when abnormal information is detected, it can be considered that there is excessive power load pressure in the current power grid and power load adjustment is required. At this time, power load data can be obtained from the data acquisition device through the interface of the data acquisition device so that the power load adjustment plan can be predicted based on the power load data.

[0082] Step S506, performing prediction based on the real-time power load data through a prediction model to obtain a power load adjustment plan, wherein the prediction model is constructed based on historical power load data.

[0083] In an optional embodiment, the historical power load data can be used as training data to train the model so that the model has the ability to predict the power load regulation scheme based on the power load data to obtain a prediction model. The real-time power load data is input into the trained prediction model, and the prediction model predicts the power load regulation method to obtain the power load regulation scheme.

[0084] Step S508: regulating the power grid based on the power load regulation scheme.

[0085] In an optional embodiment, the implementation of the power load regulation scheme usually involves adjustments at multiple levels. In terms of power output, the power load regulation scheme can be used to adjust the output of power generation to ensure that the power supply matches the predicted demand. Then, in terms of power distribution, the power distribution can be adjusted according to the power load regulation scheme to ensure a stable supply of power. In addition, in terms of demand response, users can be encouraged to reduce power consumption during predicted peak power consumption periods based on the power load regulation scheme to balance the load on the power grid.

[0086] In one embodiment of the present application, a prediction is performed based on real-time power load data through a prediction model to obtain a power load regulation plan, including: inputting real-time power load data into the prediction model, using the prediction model to predict the power load regulation plan to obtain a preliminary power load regulation plan; adjusting the preliminary power load regulation plan to obtain a power load regulation plan.

[0087] In an optional embodiment, the historical power load data can be used to train the model to obtain a prediction model with prediction capabilities, and the real-time power load data can be input into the prediction model to obtain a preliminary power load regulation plan by using the prediction model for prediction. On this basis, the load regulation plan can also be manually adjusted in a refined manner with reference to the load change law generated according to the historical power load data to obtain a power load regulation plan.

[0088] In one implementation of the present application, a preliminary power load regulation plan is adjusted to obtain a power load regulation plan, including: determining multiple daily average adjustable loads based on historical power load data; modeling the change trend of power load data according to the multiple daily average adjustable loads to obtain a load change law; converting the load change law into a visual chart; and adjusting the preliminary power load regulation plan based on the visual chart to obtain a power load regulation plan.

[0089] In an optional embodiment, the adjustable load at each time point of each day in the historical period is determined based on the historical power load data, and then the average value of the adjustable load at each time point of each day is taken with the day as the statistical unit to obtain the daily average adjustable load. Use the time series analysis method to model multiple daily average adjustable load values ​​to obtain the load change law to identify the law and trend of load change. Determine the type of visualization chart, and the types of visualization charts that can be selected include but are not limited to time series charts, bar charts, heat maps or scatter plots, etc. You can choose a suitable type according to the actual application situation, and the specific type of visualization chart is not limited here. Map the obtained load change law to the selected chart to obtain a visualization chart.

[0090] After converting the load variation law into a load variation visualization chart, the user can further understand the load variation law based on the intuitive visualization chart, compare the preliminary power load regulation plan with the visualization chart showing the load variation law, check whether the preliminary power load regulation plan has fully considered the load variation law, and manually perform fine-grained fine-tuning on the preliminary power load regulation plan based on the load variation law displayed in the visualization chart, so as to obtain a more functionally complete power load regulation plan.

[0091] In one embodiment of the present application, the power load regulation method also includes: when obtaining the collected power load data from the data acquisition device, judging the access permission; if the access permission is a preset permission, the data acquisition device allows the acquisition of the power load data; if the access permission is not a preset permission, the data acquisition device prohibits the acquisition of the power load data.

[0092] In an optional embodiment, when generating a power load regulation scheme, for security reasons, the power load data used to generate the power load regulation scheme cannot be obtained under any circumstances. Before obtaining the power load data in the data acquisition device, it is necessary to first determine the user's access rights. The determination of access rights can be completed by account password login and verification code login, wherein the user terminal corresponding to the account password login is a tablet computer, mobile phone or laptop computer, and the user terminal corresponding to the verification code login is a mobile phone. If the user enters the account number and the password matching the account number through the user terminal, or enters the account number and the verification code matching the account number, the task access right can be a preset right. At this time, it can be allowed to obtain the power load data from the data acquisition device for generating the power load regulation scheme. Otherwise, it is prohibited to obtain the power load data from the data acquisition device.

[0093] Example 3

[0094] According to an embodiment of the present invention, there is also provided an electronic device, comprising: a memory storing an executable program; and a processor for running the program, wherein the power load regulation method in Embodiment 2 is executed when the program is running.

[0095] Example 4

[0096] An embodiment of the present application further provides a computer-readable storage medium, which includes a stored executable program, wherein when the executable program is running, the device where the computer-readable storage medium is located is controlled to execute the power load regulation method in each embodiment of the present invention.

[0097] Example 5

[0098] An embodiment of the present application further provides a computer program product, including a computer program, which implements the power load regulation method in each embodiment of the present invention when executed by a processor.

[0099] Example 6

[0100] An embodiment of the present application also provides a computer program product, including a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium is used to store a computer program, and when the computer program is executed by a processor, the power load regulation method in each embodiment of the present invention is implemented.

[0101] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0102] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0103] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0104] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0105] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0106] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0107] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A power load regulation system, characterized in that: include: an abnormality monitoring device, used to monitor the operation state of the power grid, and generate abnormality information when the operation state of the power grid is abnormal; A data collection device, comprising a plurality of power load data collection units, for collecting the power load data from a plurality of data sources in the power grid, wherein the plurality of data sources at least include: an electric meter and a sensor, and the power load data includes real-time power load data and historical power load data; A dispatching control device is respectively connected to the abnormality monitoring device and the data acquisition device, and is used for obtaining the collected power load data from the data acquisition device based on the abnormal information after receiving the abnormal information, making predictions through a prediction model based on the power load data to obtain a power load adjustment plan, and adjusting the power grid based on the power load adjustment plan, wherein the prediction model is constructed based on the historical power load data.

2. The power load regulation system according to claim 1, characterized in that: The dispatching control device comprises: A preprocessing unit, connected to the abnormality monitoring device and the data acquisition device, respectively, for obtaining the real-time power load data from the data acquisition device after receiving the abnormality information, performing a preprocessing operation on the real-time power load data, and obtaining data after the preprocessing operation; A model building unit, connected to the data acquisition device, used to obtain the historical power load data from the data acquisition device, and use the historical power load data to train a preset model to obtain the prediction model; A prediction unit is connected to the preprocessing unit and the model building unit, and is used to perform prediction through the prediction model based on the data after the preprocessing operation to obtain the power load adjustment plan.

3. The power load regulation system according to claim 2, characterized in that: The pre-processing unit comprises: A data cleaning module, used to identify and remove abnormal values ​​and missing values ​​in the real-time power load data, and obtain the real-time power load data after the data cleaning operation; The feature extraction module is used to perform a feature extraction operation on the real-time power load data after the data cleaning operation to obtain the data after the preprocessing operation.

4. The power load regulation system according to claim 1, characterized in that: The power load regulation system further includes a reference adjustment device, wherein the reference adjustment device includes: A rule generating unit, connected to the data acquisition device, for generating a load variation rule according to the historical power load data; A data visualization unit is connected to the dispatching control device and is used to convert the load variation law into a visualization chart, wherein the visualization chart is used to adjust the power load regulation plan.

5. The power load regulation system according to claim 1, characterized in that: The rule generating unit comprises: A determination module, configured to determine a plurality of daily average adjustable loads based on the historical power load data; A modeling module is connected to the determination module and is used to model the change trend of the power load data according to the multiple daily average adjustable loads to obtain the load change law.

6. The electric load regulation system according to claim 1, characterized in that: The power load regulation system further includes a data encryption device, wherein the data encryption device includes: An encryption unit, used to set the data acquisition device to an encryption state that prohibits obtaining the power load data; A judging unit, configured to judge whether the access permission is a preset permission after the data acquisition device is set to an encrypted state that prohibits obtaining the power load data; The decryption unit is connected to the judgment unit and is used to set the data acquisition device to a decryption state allowing the acquisition of the power load data when the access permission is a preset permission.

7. A method for regulating electric load, characterized in that: include: monitoring the operation state of the power grid, and generating abnormal information when the operation state of the power grid is abnormal; Based on the abnormal information, acquiring collected power load data from a data collection device, wherein the power load data includes real-time power load data and historical power load data; Based on the real-time power load data, prediction is performed using a prediction model to obtain a power load adjustment plan, wherein the prediction model is constructed based on the historical power load data; The power grid is regulated based on the power load regulation scheme.

8. The power load regulation method according to claim 7, characterized in that: Based on the real-time power load data, prediction is performed through a prediction model to obtain a power load adjustment plan, including: Inputting the real-time power load data into the prediction model, using the prediction model to predict the power load regulation plan, and obtaining a preliminary power load regulation plan; The preliminary power load regulation plan is adjusted to obtain the power load regulation plan.

9. The power load regulation method according to claim 8, characterized in that: The preliminary power load regulation scheme is adjusted to obtain the power load regulation scheme, including: Determining a plurality of daily average adjustable loads based on the historical power load data; Modeling the change trend of the power load data according to the multiple daily average adjustable loads to obtain a load change law; Convert the load variation law into a visual chart; The preliminary power load regulation plan is adjusted based on the visualization chart to obtain the power load regulation plan.

10. The power load regulation method according to any one of claims 7 to 9, characterized in that: The power load regulation method further comprises: When acquiring the collected power load data from the data collection device, judging the access authority; If the access permission is a preset permission, the data acquisition device allows the acquisition of the power load data; If the access permission is not a preset permission, the data acquisition device prohibits the acquisition of the power load data.

11. An electronic device, characterized in that: include: A memory storing an executable program; A processor is used to run the program, wherein the program, when running, executes the power load regulation method described in any one of claims 7 to 10.

12. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored executable program, wherein when the executable program is executed, the device where the storage medium is located is controlled to execute the power load regulation method according to any one of claims 7 to 10.

13. A computer program product, characterized in that The invention comprises a computer program, which implements the power load adjustment method according to any one of claims 7 to 10 when being executed by a processor.

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