Power grid peak regulation method and device

Abstract

The invention discloses a power grid peak regulation method and device, and the method comprises the steps: obtaining the historical operation data of a power grid, and carrying out the analysis of the historical operation data, and determining key impact factors which affect the load of the power grid; constructing a power grid load prediction model based on the key influence factors, and predicting load prediction data of the power grid; performing data analysis on the load prediction data of the power grid to determine a load peak period of the power grid; obtaining the power generation capacity of the power grid, evaluating the load peak period of the power grid based on the power generation capacity and the load prediction data, and determining the load level of the power grid according to the evaluation result; and formulating a peak regulation strategy of the power grid according to the load level of the power grid to carry out peak regulation on the power grid. The load state of the power grid is determined by evaluating the load level of the power grid, and the corresponding peak regulation strategy is made according to the load level to quickly regulate the peak of the power grid, so that the balance of power supply and power utilization during a high load period is ensured, and the condition of power grid fault or power failure caused by unbalanced power supply and demand is avoided; and reliability and stability of a power grid are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of power generation, and in particular to a power grid peak regulation method and device. Background Art

[0002] Grid peak load regulation refers to a method of maintaining stable operation of the power grid by adjusting the balance between power generation and load when the load of the power system reaches its peak. Various measures are used to cope with the power demand during the load peak to ensure the stable operation of the system. In the power system, the load usually fluctuates over time. In certain specific time periods, such as the peak industrial production period during the day or the peak air conditioning power consumption period in hot summer weather, the power grid may face greater load pressure, and even the load may reach a peak, which may cause the power grid to be overloaded or insufficient. Therefore, in order to cope with these load peaks, the power grid needs to take a series of measures to ensure that it can meet the power needs of users and maintain the stable operation of the power grid. The goal of the peak load regulation strategy is to balance the load demand through various technologies and measures without increasing the system capacity to ensure the reliability and stability of the system.

[0003] However, traditional power grid peak-shaving methods usually only focus on how to achieve peak-shaving, but do not pay attention to how to determine the current load level of the power grid and how to accurately implement peak-shaving measures based on the load level of the power grid. As a result, it is impossible to quickly peak-shave the power grid, and it is difficult to ensure the balance between power supply and power consumption during high-load periods. The imbalance between power supply and demand leads to frequent power grid failures or power outages, and the reliability and stability of the power grid cannot be ensured. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a power grid peak regulation method and device, including:

[0005] Obtain historical operation data of the power grid, analyze the historical operation data, and determine the key factors affecting the power grid load;

[0006] Construct a power grid load prediction model based on key influencing factors, and make predictions based on the power grid load prediction model to obtain power grid load prediction data;

[0007] Analyze the load forecast data of the power grid to determine the peak load period of the power grid;

[0008] Obtain the power generation capacity of the power grid, evaluate the peak load period of the power grid based on the power generation capacity and load forecast data, and determine the load level of the power grid according to the evaluation results;

[0009] Formulate a peak-shaving strategy for the power grid according to the load level of the power grid, and perform peak-shaving on the power grid according to the peak-shaving strategy.

[0010] Furthermore, the acquisition of historical operation data of the power grid and analysis of the historical operation data to determine key factors affecting the load of the power grid include:

[0011] Obtain historical operation data of the power grid, and determine historical load data from the historical operation data;

[0012] Conduct cluster analysis on historical load data to determine the general factors affecting grid load;

[0013] The correlation between the data corresponding to the general influencing factors and the historical load data is calculated, and the influencing factors with a correlation greater than a preset threshold are determined as key influencing factors affecting the power grid load.

[0014] Furthermore, the power grid load forecasting model is constructed based on the key influencing factors, and forecasting is performed according to the power grid load forecasting model to obtain power grid load forecasting data, including:

[0015] Acquire data corresponding to key influencing factors to establish a data set, and build an initial load prediction model for the power grid based on the data set and a preset neural network model;

[0016] The data set is divided into a training set and a test set according to a certain ratio, and the training set and the test set are input into the initial load prediction model, and the initial load prediction model is trained and tested until the initial load prediction model meets the preset convergence conditions, thereby obtaining a power grid load prediction model;

[0017] The load forecasting model of the power grid is used to make forecasts and obtain the load forecasting data of the power grid.

[0018] Furthermore, the data analysis of the load forecast data of the power grid to determine the load peak period of the power grid includes:

[0019] Obtain load forecast data of the power grid and draw a load-time curve based on the load forecast data;

[0020] Determine the peak time node where the load is at the peak value and the valley time node where the load is at the valley value from the load-time curve, and divide the load-time curve into a plurality of sub-load-time curves in sequence according to the valley time node-peak time node-peak time node;

[0021] The average value of the load data in each sub-load-time curve is calculated, the time period in which the load data in each sub-load-time curve exceeds the load average value is determined, and these time periods are determined as the load peak period of the power grid.

[0022] Furthermore, the obtaining of the power generation capacity of the power grid, evaluating the load peak period of the power grid based on the power generation capacity and load forecast data, and determining the load level of the power grid according to the evaluation result, includes:

[0023] Obtain the power generation capacity of the power grid, calculate the difference between the power generation capacity and the load peak value in each load peak period, and use the difference as the load difference in the load peak period;

[0024] Obtain the duration of each load peak period and the amount of load data exceeding the load average value, and set the weight coefficient of the load peak period according to the duration and the amount of load data;

[0025] The load difference of each load peak period is weighted and added with the corresponding weight coefficient to obtain the load value of the power grid;

[0026] The load level of the power grid is determined according to the load value of the power grid.

[0027] Further, determining the load level of the power grid according to the load value of the power grid includes:

[0028] A load level-load value interval correspondence relationship is preset, and the load level-load value interval correspondence relationship is associated with a corresponding load level for each load value interval of the operation monitoring data;

[0029] The load value of the power grid is obtained, and based on the mapping relationship between the load value interval to which the load value belongs and the load level-load value interval corresponding relationship, the load level corresponding to the power grid is determined.

[0030] Furthermore, the weight coefficient of the load peak period is set according to the time length and the load data volume, including:

[0031] The weight coefficient of the load peak period is set according to the time length and load data volume. The calculation formula of the weight coefficient is:

[0032]

[0033] Among them, q is the weight coefficient, exp is the natural exponential function, d is the time length, d0 is the preset time length, s is the load data volume, and s0 is the preset load data volume.

[0034] Furthermore, formulating a peak load regulation strategy for the power grid according to the load level of the power grid, and performing peak load regulation on the power grid according to the peak load regulation strategy, includes:

[0035] The load level of the power grid is divided into three levels, each level corresponds to overload state, high load state and low load state;

[0036] If the grid is in a low-load state, the output of the generator set is increased to peak load the grid;

[0037] If the grid is in a high-load state, the number of generator sets is increased, and the output of the generator sets is increased at the same time to peak load the grid;

[0038] If the power grid is overloaded, the number of generator sets will be increased, the output of the generator sets will be increased, and the energy released by the energy storage facilities will be introduced into the power grid to peak regulate the power grid.

[0039] The present invention also provides a power grid peak load regulation device, comprising:

[0040] An acquisition module is used to acquire historical operation data of the power grid, analyze the historical operation data, and determine the key influencing factors affecting the power grid load;

[0041] A prediction module is used to construct a power grid load prediction model based on key influencing factors, and to perform predictions based on the power grid load prediction model to obtain power grid load prediction data;

[0042] An analysis module is used to analyze the load forecast data of the power grid and determine the load peak period of the power grid;

[0043] An evaluation module is used to obtain the power generation capacity of the power grid, evaluate the load peak period of the power grid based on the power generation capacity and load forecast data, and determine the load level of the power grid according to the evaluation results;

[0044] The regulation module is used to formulate a peak load regulation strategy for the power grid according to the load level of the power grid, and to perform peak load regulation on the power grid according to the peak load regulation strategy.

[0045] Compared with the prior art, the power grid peak regulation method and device according to the embodiment of the present invention have the following beneficial effects:

[0046] The present invention determines the load status of the power grid by evaluating the load level of the power grid, and formulates a corresponding peak-shaving strategy according to the load level to quickly peak-shave the power grid, thereby ensuring the balance between power supply and power consumption during high-load periods, avoiding power grid failures or power outages caused by an imbalance in power supply and demand, and ensuring the reliability and stability of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 1 is a schematic diagram of the process structure of the power grid peak regulation method in an embodiment of the present invention;

[0048] Figure 2 It is a schematic diagram of the composition of the power grid peak load regulation device in an embodiment of the present invention. DETAILED DESCRIPTION

[0049] The specific implementation methods of the present application are further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0050] In the description of the present application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship are the orientation or position relationship shown in the accompanying drawings, which are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the platform or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0051] The terms "second" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, a feature defined with "second" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "multiple" means two or more.

[0052] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0053] like Figure 1 As shown, in an embodiment of the present application, a power grid peak-shaving method is provided, including: S100: acquiring historical operation data of the power grid, and analyzing the historical operation data to determine key influencing factors affecting the power grid load; S200: constructing a power grid load prediction model based on the key influencing factors, and performing predictions based on the power grid load prediction model to obtain power grid load prediction data; S300: performing data analysis on the power grid load prediction data to determine the peak load period of the power grid; S400: acquiring the power generation capacity of the power grid, evaluating the peak load period of the power grid based on the power generation capacity and the load prediction data, and determining the load level of the power grid according to the evaluation results; S500: formulating a power grid peak-shaving strategy according to the load level of the power grid, and performing peak-shaving on the power grid according to the peak-shaving strategy.

[0054] Furthermore, the present invention determines the load status of the power grid by evaluating the load level of the power grid, and formulates a corresponding peak-shaving strategy according to the load level to quickly peak-shave the power grid, thereby ensuring a balance between power supply and power consumption during high-load periods, avoiding power grid failures or power outages caused by an imbalance in power supply and demand, and ensuring the reliability and stability of the power grid.

[0055] In an embodiment of the present application, a power grid peak-shaving method is provided, wherein historical operation data of the power grid is obtained, and the historical operation data is analyzed to determine key influencing factors affecting the power grid load, including: obtaining historical operation data of the power grid, and determining historical load data from the historical operation data; performing cluster analysis on the historical load data to determine general influencing factors affecting the power grid load; calculating the correlation between data corresponding to the general influencing factors and the historical load data, and determining the influencing factors having a correlation greater than a preset threshold as key influencing factors affecting the power grid load.

[0056] Specifically, historical operation data of the power grid is obtained from relevant institutions, and load data is extracted from the obtained data; general influencing factors are determined by cluster analysis, and cluster analysis methods (such as K-means clustering, hierarchical clustering, etc.) are used to cluster historical load data to determine different load patterns or types, and general factors that may affect the load pattern are determined from the clustering results; the correlation between the influencing factors and historical load data is calculated, and the correlation between the influencing factors and historical load data is calculated using correlation analysis methods (such as Pearson correlation coefficient, Spearman rank correlation coefficient, etc.); a correlation threshold is preset to screen the influencing factors, and the influencing factors with a correlation greater than the preset threshold are determined as key influencing factors affecting the power grid load. Through these steps, the key influencing factors that affect the power grid load can be determined from historical data, and these factors will help to formulate more accurate load forecasting models and peak-shaving strategies.

[0057] In an embodiment of the present application, a power grid peak-shaving method is provided, wherein a power grid load forecasting model is constructed based on key influencing factors, and prediction is performed according to the power grid load forecasting model to obtain load forecasting data of the power grid, including: acquiring data corresponding to the key influencing factors to establish a data set, and constructing an initial load forecasting model of the power grid according to the data set and a preset neural network model; dividing the data set into a training set and a test set according to a certain ratio, and inputting the training set and the test set into the initial load forecasting model, training and testing the initial load forecasting model until the initial load forecasting model meets the preset convergence conditions to obtain the power grid load forecasting model; and predicting through the power grid load forecasting model to obtain load forecasting data of the power grid.

[0058] Specifically, the data corresponding to the key influencing factors obtained are established as a data set, ensuring that the data set includes input features and corresponding load data; constructing an initial load prediction model for the power grid based on the data set and a preset neural network model; dividing the data set into a training set and a test set according to a certain ratio, using a common division ratio such as 70% training set and 30% test set, inputting the training set into the initial load prediction model, and training the model until the preset convergence conditions are met to ensure that the model can fit the training data well; and performing predictions through the power grid load prediction model to obtain the load prediction data of the power grid.

[0059] In an embodiment of the present application, a power grid peak regulation method is provided, wherein data analysis is performed on load forecast data of the power grid to determine the load peak period of the power grid, including: obtaining the load forecast data of the power grid, and drawing a load-time curve based on the load forecast data; determining a peak time node where the load is at a peak value and a valley time node where the load is at a valley value from the load-time curve, and dividing the load-time curve into a plurality of sub-load-time curves in sequence of valley time node-peak time node-peak time node; calculating the average value of the load data in each sub-load-time curve, determining the time period in which the load data in each sub-load-time curve exceeds the load average value, and determining these time periods as the load peak period of the power grid.

[0060] Specifically,

[0061] In an embodiment of the present application, a power grid peak-shaving method is provided, which obtains the power generation capacity of the power grid, evaluates the load peak period of the power grid based on the power generation capacity and load forecast data, and determines the load level of the power grid according to the evaluation result, including: obtaining the power generation capacity of the power grid, calculating the difference between the power generation capacity and the load peak in each load peak period, and using the difference as the load difference in the load peak period; obtaining the time length of each load peak period and the amount of load data exceeding the load average, and setting the weight coefficient of the load peak period according to the time length and the load data amount; performing weighted addition calculation on the load difference of each load peak period and the corresponding weight coefficient to obtain the load value of the power grid; and determining the load level of the power grid according to the load value of the power grid.

[0062] Specifically, load forecast data is obtained from the load forecast model, and the load-time curve is drawn using the obtained load forecast data to show the load change trend over time; the peak time node where the load is at the peak value and the valley time node where the load is at the valley value are determined from the load-time curve; the load-time curve is divided into several sub-load-time curves according to the valley time node-peak time node-peak time node; for each sub-load-time curve, the average value of the load data is calculated to determine the average load level in the time period; the time period in which the load data exceeds the load average value in each sub-load-time curve is determined, and these time periods are determined as the peak load period of the power grid. Through these steps, the load data can be analyzed in detail, the load peak and valley time nodes can be determined, and the peak load period of the power grid can be further determined, which is helpful to formulate targeted peak-shaving strategies and measures to deal with load peaks.

[0063] In an embodiment of the present application, a power grid peak-shaving method is provided, wherein the load level of the power grid is determined according to the load value of the power grid, including: a load level-load value interval correspondence is pre-set, and each load value interval of the load level-load value interval correspondence is associated with a corresponding load level for each load value interval of operation monitoring data; the load value of the power grid is obtained, and based on a mapping relationship between the load value interval to which the load value belongs within the load level-load value interval correspondence, the corresponding load level of the power grid is determined.

[0064] Specifically, the information of power generation capacity is obtained from the power grid data; the difference between the power generation capacity and the load peak in each load peak period is calculated respectively, and the difference is used as the load difference in the load peak period; the time length and load data volume of the load peak period are obtained to set the weight coefficient of the load peak period to reflect the importance of different load peak periods; the load difference of each load peak period is weighted and added with the corresponding weight coefficient to obtain the load value of the power grid; the load level of the power grid is determined according to the load value of the power grid. Through these steps, the load value of the power grid can be calculated according to factors such as power generation capacity, load difference, weight coefficient, etc., and the load level of the power grid can be further determined, which is helpful for evaluating and classifying the load situation of the power grid and providing an important reference for the operation and dispatch of the power grid.

[0065] In an embodiment of the present application, a power grid peak regulation method is provided, wherein the weight coefficient of the load peak period is set according to the time length and the load data volume, including: setting the weight coefficient of the load peak period according to the time length and the load data volume, and the calculation formula of the weight coefficient is,

[0066]

[0067] Among them, q is the weight coefficient, exp is the natural exponential function, d is the time length, d0 is the preset time length, s is the load data volume, and s0 is the preset load data volume.

[0068] In an embodiment of the present application, a power grid peak-shaving method is provided, wherein a power grid peak-shaving strategy is formulated according to the load level of the power grid, and the power grid is peak-shaving according to the peak-shaving strategy, including: the load level of the power grid is divided into three levels, each level corresponds to an overload state, a high load state and a low load state; if the power grid is in a low load state, the output of the generator set is increased to peak-shave the power grid; if the power grid is in a high load state, the number of generator sets is increased, and the output of the generator sets is increased at the same time to peak-shave the power grid; if the power grid is in an overload state, the number of generator sets is increased, and the output of the generator sets is increased at the same time, and the energy storage released by the energy storage facility is introduced into the power grid to peak-shave the power grid.

[0069] Specifically, when the grid is in a low-load state, the output of the generator sets is increased to peak the grid; when the grid is in a high-load state, the number of generator sets is increased, and the output of the generator sets is increased at the same time to peak the grid; when the grid is in an overload state, the number of generator sets is increased, and the output of the generator sets is increased at the same time, and the energy storage released by the energy storage facilities is introduced into the grid to peak the grid. In an overload state, it is necessary not only to increase the number and output of the generator sets, but also to use energy storage facilities to alleviate the overload of the grid. The above action plan will help to take corresponding peak-shaving measures according to the load level of the grid to ensure the stable operation and load balance of the grid.

[0070] like Figure 2 As shown, in an embodiment of the present application, a power grid peak-shaving device is provided, including: an acquisition module, used to acquire historical operation data of the power grid, and analyze the historical operation data to determine key influencing factors affecting the power grid load; a prediction module, used to construct a power grid load prediction model based on the key influencing factors, and predict according to the power grid load prediction model to obtain the load prediction data of the power grid; an analysis module, used to perform data analysis on the load prediction data of the power grid to determine the peak load period of the power grid; an evaluation module, used to acquire the power generation capacity of the power grid, evaluate the peak load period of the power grid based on the power generation capacity and the load prediction data, and determine the load level of the power grid according to the evaluation results; an adjustment module, used to formulate a peak-shaving strategy for the power grid according to the load level of the power grid, and peak-shave the power grid according to the peak-shaving strategy.

[0071] In summary, the embodiments of the present invention provide a power grid peak-shaving method and device, which include: obtaining historical operation data of the power grid, and analyzing it to determine key influencing factors that affect the power grid load; constructing a power grid load prediction model based on the key influencing factors, and predicting the load prediction data of the power grid; performing data analysis on the load prediction data of the power grid to determine the peak load period of the power grid; obtaining the power generation capacity of the power grid, and evaluating the peak load period of the power grid based on it and the load prediction data, and determining the load level of the power grid according to the evaluation results; formulating a power grid peak-shaving strategy according to the load level of the power grid to perform peak-shaving on the power grid. The present invention evaluates the load level of the power grid, determines the load state of the power grid, and formulates a corresponding peak-shaving strategy according to the load level to quickly peak-shave the power grid, thereby ensuring the balance between power supply and power consumption during high load periods, avoiding power grid failures or power outages caused by imbalance in power supply and demand, and ensuring the reliability and stability of the power grid.

[0072] Finally, it should be noted that it is obvious that those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

[0073] The above is only an example of implementation of the present invention, but it cannot be used to limit the scope of the present invention. Any structural changes made according to the present invention, as long as they do not lose the essence of the present invention, should be regarded as falling within the scope of protection of the present invention and being restricted. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process and related instructions of the platform described above can refer to the corresponding process in the aforementioned platform embodiment, and will not be repeated here.

[0074] The term "comprises" or any other similar term is intended to cover a non-exclusive inclusion such that a process, platform, article, or apparatus / platform that includes a list of elements includes not only those elements but also other elements not expressly listed or inherent to such process, platform, article, or apparatus / platform.

[0075] So far, the technical solutions of the present invention have been described in conjunction with the further embodiments shown in the accompanying drawings, but it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0076] The above description is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention.

Claims

1. A power grid peak regulation method, characterized in that: include: Obtain historical operation data of the power grid, analyze the historical operation data, and determine the key factors affecting the power grid load; Construct a power grid load prediction model based on key influencing factors, and make predictions based on the power grid load prediction model to obtain power grid load prediction data; Analyze the load forecast data of the power grid to determine the peak load period of the power grid; Obtain the power generation capacity of the power grid, evaluate the peak load period of the power grid based on the power generation capacity and load forecast data, and determine the load level of the power grid according to the evaluation results; Formulate a peak-shaving strategy for the power grid according to the load level of the power grid, and perform peak-shaving on the power grid according to the peak-shaving strategy.

2. A power grid peak load regulation method according to claim 1, characterized in that: The acquisition of historical operation data of the power grid and analysis of the historical operation data to determine key factors affecting the power grid load include: Obtain historical operation data of the power grid, and determine historical load data from the historical operation data; Conduct cluster analysis on historical load data to determine the general factors affecting grid load; The correlation between the data corresponding to the general influencing factors and the historical load data is calculated, and the influencing factors with a correlation greater than a preset threshold are determined as key influencing factors affecting the power grid load.

3. A power grid peak load regulation method according to claim 2, characterized in that: The power grid load prediction model is constructed based on the key influencing factors, and prediction is performed according to the power grid load prediction model to obtain power grid load prediction data, including: Acquire data corresponding to key influencing factors to establish a data set, and build an initial load prediction model for the power grid based on the data set and a preset neural network model; The data set is divided into a training set and a test set according to a certain ratio, and the training set and the test set are input into the initial load prediction model, and the initial load prediction model is trained and tested until the initial load prediction model meets the preset convergence conditions, thereby obtaining a power grid load prediction model; The load forecasting model of the power grid is used to make forecasts and obtain the load forecasting data of the power grid.

4. A power grid peak load regulation method according to claim 3, characterized in that: The data analysis of the load forecast data of the power grid to determine the load peak period of the power grid includes: Obtain load forecast data of the power grid and draw a load-time curve based on the load forecast data; Determine the peak time node where the load is at the peak value and the valley time node where the load is at the valley value from the load-time curve, and divide the load-time curve into a plurality of sub-load-time curves in sequence according to the valley time node-peak time node-peak time node; The average value of the load data in each sub-load-time curve is calculated, the time period in which the load data in each sub-load-time curve exceeds the load average value is determined, and these time periods are determined as the load peak period of the power grid.

5. A power grid peak load regulation method according to claim 4, characterized in that: The obtaining of the power generation capacity of the power grid, evaluating the load peak period of the power grid based on the power generation capacity and load forecast data, and determining the load level of the power grid according to the evaluation result, includes: Obtain the power generation capacity of the power grid, calculate the difference between the power generation capacity and the load peak value in each load peak period, and use the difference as the load difference in the load peak period; Obtain the duration of each load peak period and the amount of load data exceeding the load average value, and set the weight coefficient of the load peak period according to the duration and the amount of load data; The load difference of each load peak period is weighted and added with the corresponding weight coefficient to obtain the load value of the power grid; The load level of the power grid is determined according to the load value of the power grid.

6. A power grid peak load regulation method according to claim 5, characterized in that: Determining the load level of the power grid according to the load value of the power grid includes: A load level-load value interval correspondence relationship is preset, and the load level-load value interval correspondence relationship is associated with a corresponding load level for each load value interval of the operation monitoring data; The load value of the power grid is obtained, and based on the mapping relationship between the load value interval to which the load value belongs and the load level-load value interval corresponding relationship, the load level corresponding to the power grid is determined.

7. A power grid peak load regulation method according to claim 5, characterized in that: The weight coefficient of the load peak period is set according to the time length and the load data volume, including: The weight coefficient of the load peak period is set according to the time length and load data volume. The calculation formula of the weight coefficient is: Among them, q is the weight coefficient, exp is the natural exponential function, d is the time length, d0 is the preset time length, s is the load data volume, and s0 is the preset load data volume.

8. A power grid peak load regulation method according to claim 5, characterized in that: The formulating a peak load regulation strategy for the power grid according to the load level of the power grid, and performing peak load regulation on the power grid according to the peak load regulation strategy, includes: The load level of the power grid is divided into three levels, each level corresponds to overload state, high load state and low load state; If the grid is in a low-load state, the output of the generator set is increased to peak load the grid; If the grid is in a high-load state, the number of generator sets is increased, and the output of the generator sets is increased at the same time to peak load the grid; If the power grid is overloaded, the number of generator sets will be increased, the output of the generator sets will be increased, and the energy released by the energy storage facilities will be introduced into the power grid to peak regulate the power grid.

9. A power grid peak load regulation device, characterized in that: include: An acquisition module is used to acquire historical operation data of the power grid, analyze the historical operation data, and determine the key influencing factors affecting the power grid load; A prediction module is used to construct a power grid load prediction model based on key influencing factors, and to perform predictions based on the power grid load prediction model to obtain power grid load prediction data; An analysis module is used to analyze the load forecast data of the power grid and determine the load peak period of the power grid; An evaluation module is used to obtain the power generation capacity of the power grid, evaluate the load peak period of the power grid based on the power generation capacity and load forecast data, and determine the load level of the power grid according to the evaluation results; The regulation module is used to formulate a peak load regulation strategy for the power grid according to the load level of the power grid, and to perform peak load regulation on the power grid according to the peak load regulation strategy.

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