Multi-scene collaborative power dispatching operation strategy optimization system and method

Abstract

The invention discloses a multi-scene collaborative power dispatching operation strategy optimization system and method, relates to the technical field of power dispatching, and solves the technical problems that the environmental protection property in the power generation process is not considered in the prior art, even if the minimum coal consumption is used for power generation, carbon emission is still generated, and the ecological environment is polluted. The method comprises the steps of obtaining real-time power data and operation environment data of a power grid; analyzing the operation scene of the power grid according to the operation environment data; predicting the power data of a plurality of links of the power grid based on the real-time power data of the power grid; scheduling the power of the power grid according to the prediction result; power dispatching operation is optimized according to the operation scene of the power grid; a green power generation mode can be used, carbon emission is reduced, and pollution of power generation to the environment is reduced while power utilization is guaranteed.

Description

Technical Field

[0001] The present invention belongs to the field of electric power dispatching and relates to electric power dispatching operation strategy optimization technology, specifically to a multi-scenario collaborative electric power dispatching operation strategy optimization system and method. Background Art

[0002] Power dispatching refers to a series of technical and management activities that conduct real-time monitoring, coordination and optimization control of the power system's power generation, transmission, distribution and power consumption to ensure the safe, stable, emergency and reliable operation of the power system; multi-scenario collaboration integrates power demand and supply information at different time scales and spatial scales to achieve dynamic allocation of power generation resources, energy storage devices and demand-side response, avoid idle or excessive resource calls, and significantly improve the overall efficiency of the system; by simulating multiple scenarios such as extreme weather, equipment failures, and load mutations, the optimized adjustment strategy can identify weak links in the equipment system in advance and formulate backup plans.

[0003] In the existing technology, power dispatching generally analyzes the minimum coal consumption of the generator set within a certain time period and adjusts the power generation of the generator set according to the minimum value. However, the existing technology does not take into account the environmental protection of the power generation process. Even if the minimum coal consumption is used, carbon emissions will still be generated, causing pollution to the ecological environment.

[0004] The present invention provides a multi-scenario coordinated power dispatching operation strategy optimization system and method to solve the above technical problems. Summary of the Invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes a multi-scenario collaborative power dispatching operation strategy optimization system and method to solve the technical problem that the prior art does not take into account the environmental protection of the power generation process, and even if the minimum coal consumption is used for power generation, carbon emissions will still be generated, resulting in pollution of the ecological environment.

[0006] To achieve the above objectives, the first aspect of the present invention provides a multi-scenario coordinated power dispatch operation strategy optimization method, comprising: Obtain real-time power data and operating environment data of the power grid; Analyze the operation scenarios of the power grid based on the operating environment data; Predict power data of several links of the power grid based on real-time power data of the power grid; Dispatch power from the power grid based on the forecast results; Optimize the dispatching and operation of electricity according to the operating scenarios of the power grid.

[0007] Preferably, analyzing the operation scenario of the power grid according to the operation environment data includes: Retrieve operating environment data; wherein the operating environment data includes: operating wind speed, operating air volume and light intensity; The wind energy coefficient of the operating environment is evaluated according to the operating wind speed and operating wind volume in the operating environment data; a plurality of scene types are obtained; the wind energy coefficient and the light intensity are matched with the plurality of scene types to obtain the operating scene of the power grid.

[0008] The present invention evaluates the wind energy coefficient of the operating environment based on the operating wind speed and operating wind volume in the operating environment data, matches the wind energy coefficient and light intensity with several scene types, and obtains the corresponding power grid operation scene. It can analyze the operation scene of the power grid and analyze the actual operating environment of the power grid, providing an application scenario for subsequent optimization of power dispatching.

[0009] Preferably, the step of evaluating the wind energy coefficient of the operating environment according to the operating wind speed and the operating wind volume in the operating environment data includes: Retrieve the operating wind speed and operating air volume from the operating environment data; obtain air data; wherein the air data includes: temperature, air pressure, and water vapor pressure; Calculate the air density of air based on air data; by the formula Calculate the wind energy coefficient of the operating environment; where ρ represents air density, Q represents operating air volume, and v represents operating wind speed; The air density is calculated as: ; Where P represents air pressure, T represents temperature, e represents water vapor pressure; and R is the gas constant.

[0010] It should be noted that the gas constant is a key physical constant in the ideal gas state equation. The gas constant in air is generally set to 287 J / (kg·K).

[0011] Preferably, the matching of the wind energy coefficient and the light intensity with several scene types includes: Retrieve the wind energy coefficient and light intensity; construct cluster centers for several scene types, and convert the wind energy coefficient and light intensity into environmental coordinates; where the wind energy coefficient is used as the horizontal coordinate and the light intensity is used as the vertical coordinate; Construct a distance analysis function: ; Where xf represents the value of the wind energy coefficient, yg represents the data of the light intensity; xi represents the wind energy coefficient corresponding to the i-th cluster center; yi represents the light intensity corresponding to the i-th cluster center; i=1, 2, ..., n, where n is a positive integer representing the number of cluster centers; The distance between the environment coordinates and several cluster centers is calculated according to the distance analysis function, and the one with the smallest distance is used as the scene type matched with the running environment data.

[0012] It should be noted that the cluster centers of several scene types are constructed based on the historical wind energy coefficient and light intensity.

[0013] The present invention constructs cluster centers of several scene types, converts wind energy coefficient and light intensity into environmental coordinates; and calculates the distance between the environmental coordinates and several cluster centers based on the distance analysis function, and takes the scene type with the smallest distance as the scene type matching the operating environment data; it can analyze the corresponding operating scenario according to the actual operating environment, which is conducive to making the power dispatch setting more in line with reality.

[0014] Preferably, the predicting of power data of several links of the power grid based on the real-time power data of the power grid includes: Retrieve real-time power data from the power grid, including real-time power generation, real-time carbon emissions, real-time market electricity prices, and real-time power consumption; Real-time power data of a set time period is sorted in chronological order to obtain a power analysis sequence; a power analysis model is called, and the power analysis sequence is input into the power analysis model to obtain a corresponding power label; the corresponding power data is matched according to the power label; wherein the power analysis model is constructed based on an artificial intelligence model; and the power label is set as a positive integer.

[0015] The present invention integrates the real-time power data of the power grid within a set time period into a power analysis sequence in chronological order, and uses a model to analyze the power analysis sequence to obtain power data of several links; it can predict the power data of each link, which is conducive to analyzing the power consumption data and avoiding the mismatch between power generation and power consumption, power outages or excess power.

[0016] Preferably, the power analysis model is constructed based on an artificial intelligence model, including: Selecting a suitable model and a deep learning framework from the artificial intelligence model; constructing the model based on the deep learning framework to obtain a constructed model; obtaining a standard data set; wherein the standard data set includes: standard input data consistent with the content attributes of the power analysis sequence, and standard output data consistent with the content attributes of the power label; The standard data set is divided into a training set, a validation set, and a test set according to a set ratio; the training set is used to train the construction model; the validation set is used to adjust the parameters of the construction model; the trained construction model is tested using the test set; when the test index is greater than the index threshold, the construction model is marked as a power analysis model; otherwise, the power analysis model is re-constructed and trained.

[0017] It should be noted that the test indicators include: accuracy, F1 score, recall rate and stability; the standard data set and division ratio are set according to expert evaluation; when the power analysis model needs to be rebuilt and trained, the model and deep learning framework can be reselected or the ratio of the standard data set can be re-divided.

[0018] Preferably, dispatching the power of the power grid according to the prediction result includes: Retrieving power data, including: predicted power generation, predicted power consumption, and predicted carbon emissions; comparing the predicted power generation with the predicted power consumption; When the predicted power consumption is less than the predicted power generation, the power generation is adjusted according to the difference between the two; otherwise, the power generation is increased according to the difference between the two; Retrieve the real-time market electricity price from the real-time power data of the power grid; adjust the power generation according to the real-time market electricity price and predicted carbon emissions.

[0019] The present invention compares the predicted power generation with the predicted power consumption and adjusts the predicted power generation according to the predicted power consumption; it can avoid the occurrence of excess power generation or local power outages, and is conducive to ensuring sufficient use of electricity for users.

[0020] Preferably, the adjusting of power generation according to the real-time market electricity price and the predicted carbon emissions includes: Retrieve the real-time market electricity price and predicted electricity consumption of the power grid; calculate the electricity profit amount based on the real-time market electricity price and predicted electricity consumption; Retrieve predicted carbon emissions and obtain carbon prices; calculate power generation costs based on carbon emissions and carbon prices; analyze grid profits based on electricity profits and power generation costs; When the profit is greater than the profit threshold, the power generation is not adjusted; otherwise, the corresponding power generation is reduced according to the predicted power consumption.

[0021] Preferably, the optimizing the dispatching operation of the power according to the operation scenario of the power grid includes: Retrieve the grid's operating scenario and analyze the operable power generation equipment based on the grid's operating scenario; power generation equipment includes wind power generation equipment, photovoltaic power generation equipment, and thermal power generation equipment; The wind energy coefficient and light intensity are retrieved, and the wind power generation of the wind power generation equipment is calculated according to the wind energy coefficient; the photovoltaic power generation of the photovoltaic power generation equipment is calculated according to the light intensity; and the power generation of the power grid is dispatched according to the wind power generation and photovoltaic power generation.

[0022] The present invention analyzes operable power generation equipment according to the operation scenario of the power grid, analyzes wind power generation and photovoltaic power generation according to the wind energy coefficient and light intensity, and dispatches power generation of the power grid according to the wind power generation and photovoltaic power generation. It can use green power generation methods to reduce carbon emissions, which is beneficial to reducing environmental pollution caused by power generation while ensuring electricity use.

[0023] The second aspect of the present invention provides a multi-scenario collaborative power dispatching operation strategy optimization system, comprising: a scenario scheduling module, and a data acquisition module and a scheduling optimization module connected thereto; Data acquisition module, used to obtain real-time power data and operating environment data of the power grid; The scenario scheduling module is used to analyze the operation scenario of the power grid based on the operation environment data; predict the power data of several links of the power grid based on the real-time power data of the power grid; and dispatch the power of the power grid according to the prediction results; The dispatch optimization module is used to optimize the dispatch operation of electricity according to the operation scenario of the power grid.

[0024] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention evaluates the wind energy coefficient of the operating environment based on the operating wind speed and operating wind volume in the operating environment data, matches the wind energy coefficient and light intensity with several scene types, and obtains the corresponding power grid operating scene. It can analyze the operating scene of the power grid and analyze the actual operating environment of the power grid, providing an application scenario for subsequent optimization of power dispatching; constructing cluster centers of several scene types, converting the wind energy coefficient and light intensity into environmental coordinates; and calculating the distance between the environmental coordinates and several cluster centers according to the distance analysis function, and taking the scene type with the smallest distance as the matching of the operating environment data; it can analyze the corresponding operating scene according to the actual operating environment, which is conducive to making the power dispatching setting more in line with reality.

[0025] 2. The present invention integrates the real-time power data of the power grid within a set time period into a power analysis sequence in chronological order, and uses a model to analyze the power analysis sequence to obtain power data of several links; it can predict the power data of each link, which is conducive to analyzing the power consumption data, avoiding the mismatch between power generation and power consumption, power outages or excess power; compares the predicted power generation with the predicted power consumption, and adjusts the predicted power generation according to the predicted power consumption; it can avoid the situation of excess power generation or power outages in local areas, which is conducive to ensuring sufficient use of electricity for users; analyzes the operable power generation equipment according to the operation scenario of the power grid, analyzes the wind power generation and photovoltaic power generation according to the wind energy coefficient and light intensity, and dispatches the power generation of the power grid according to the wind power generation and photovoltaic power generation. It can use green power generation methods to reduce carbon emissions, which is conducive to reducing the pollution of power generation to the environment while ensuring electricity consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0027] Figure 1 Schematic diagram of the overall steps of the method of the present invention; Figure 2 Schematic diagram of the scene matching step of the present invention; Figure 3 A schematic diagram of the power forecasting and dispatch optimization steps of the present invention; Figure 4 Schematic diagram of the working steps of the system of the present invention. DETAILED DESCRIPTION

[0028] The technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0029] See also Figure 1 The first embodiment of the present invention provides a multi-scenario coordinated power dispatch operation strategy optimization method, including: Obtain real-time power data and operating environment data of the power grid; Analyze the operation scenarios of the power grid based on the operating environment data; Predict power data of several links of the power grid based on real-time power data of the power grid; Dispatch power from the power grid based on the forecast results; Optimize the dispatching and operation of electricity according to the operating scenarios of the power grid.

[0030] See also Figure 2 , obtain the real-time power data and operating environment data of the power grid; the operating environment data includes: operating wind speed, operating wind volume and light intensity; retrieve the operating wind speed and operating wind volume in the operating environment data; obtain the air data; the air data includes: temperature, air pressure and water vapor pressure; calculate the air density of the air according to the air data; use the formula Calculate the wind energy coefficient of the operating environment; where ρ represents air density, Q represents operating air volume, and v represents operating wind speed. The air density is calculated as follows: ; Where P represents air pressure, T represents temperature, e represents water vapor pressure; and R is the gas constant.

[0031] Get several scene types; retrieve wind energy coefficient and light intensity; construct cluster centers for several scene types, convert wind energy coefficient and light intensity into environmental coordinates; where wind energy coefficient is used as the horizontal coordinate and light intensity is used as the vertical coordinate; construct a distance analysis function: ; Among them, xf represents the numerical value of the wind energy coefficient, yg represents the data of light intensity; xi represents the wind energy coefficient corresponding to the i-th cluster center; yi represents the light intensity corresponding to the i-th cluster center; i=1, 2,…, n, n is a positive integer, representing the number of cluster centers; the distance between the environmental coordinates and several cluster centers is calculated according to the distance analysis function, and the one with the smallest distance is used as the scene type matching the operating environment data.

[0032] For example: Assume that the wind energy coefficient is calculated and the wind speed v=10m / s and the wind volume Q=1500m 3 / s; the air density calculated according to the calculation formula is 1.22kg / m 3 ; According to the formula, the wind energy coefficient is FN=915kW; The wind energy coefficient and light intensity are converted into environmental coordinates, and the distances from the environmental coordinates to the three cluster centers are calculated respectively as D1, D2, and D3; the scene type corresponding to D2 with the smallest distance is selected as the operating scene that matches the operating environment data.

[0033] See also Figure 3, retrieve the real-time power data of the power grid; wherein the real-time power data includes: real-time power generation, real-time carbon emission data, real-time market electricity price and real-time power consumption; sort the real-time power data of the set time period in chronological order to obtain a power analysis sequence; call the power analysis model, input the power analysis sequence into the power analysis model, and obtain the corresponding power label; match the corresponding power data according to the power label; wherein the power analysis model is built based on the artificial intelligence model; the power label is set to a positive integer.

[0034] It is worth noting that the power analysis model is built based on an artificial intelligence model, including: Selecting a suitable model and a deep learning framework from the artificial intelligence model; constructing the model based on the deep learning framework to obtain a constructed model; obtaining a standard data set; wherein the standard data set includes: standard input data consistent with the content attributes of the power analysis sequence, and standard output data consistent with the content attributes of the power label; The standard data set is divided into a training set, a validation set, and a test set according to a set ratio; the training set is used to train the construction model; the validation set is used to adjust the parameters of the construction model; the trained construction model is tested using the test set; when the test index is greater than the index threshold, the construction model is marked as a power analysis model; otherwise, the power analysis model is re-constructed and trained.

[0035] It should be noted that the test indicators include: accuracy, F1 score, recall rate and stability; the standard data set and division ratio are set according to expert evaluation; when the power analysis model needs to be rebuilt and trained, the model and deep learning framework can be reselected or the ratio of the standard data set can be re-divided.

[0036] Retrieve power data; the power data includes: predicted power generation, predicted power consumption, and predicted carbon emissions; compare the predicted power generation with the predicted power consumption; when the predicted power consumption is less than the predicted power generation, adjust the power generation according to the difference between the two; otherwise, increase the power generation according to the difference between the two; retrieve the real-time market electricity price from the real-time power data of the power grid; adjust the power generation according to the real-time market electricity price and the predicted carbon emissions.

[0037] Retrieve the real-time market electricity price and predicted electricity consumption of the power grid; calculate the electricity profit amount based on the real-time market electricity price and predicted electricity consumption; retrieve the predicted carbon emissions and obtain the carbon price; calculate the power generation cost based on the carbon emissions and the carbon price; analyze the profit of the power grid based on the electricity profit amount and the power generation cost; when the profit is greater than the profit threshold, do not adjust the power generation; otherwise, reduce the corresponding power generation according to the predicted electricity consumption.

[0038] The operation scenario of the power grid is retrieved, and the operable power generation equipment is analyzed based on the operation scenario of the power grid; the power generation equipment includes: wind power generation equipment, photovoltaic power generation equipment and thermal power generation equipment; the wind energy coefficient and light intensity are retrieved, and the wind power generation of the wind power generation equipment is calculated based on the wind energy coefficient; the photovoltaic power generation of the photovoltaic power generation equipment is calculated based on the light intensity; and the power generation of the power grid is dispatched based on the wind power generation and photovoltaic power generation.

[0039]

[0040] Table 1: Schematic diagram of power generation equipment corresponding to power generation

[0041] For example, the power generation of the power grid is dispatched according to the power generation of wind power generation equipment and photovoltaic power generation equipment. According to the forecast power consumption, 1000 power generation capacity is required. Taking into account the existing errors and possible emergencies, a total of 1300 power generation capacity is required. Therefore, the power generation capacity of the thermal power generation equipment is adjusted from the original 1300 to 774.

[0042] See also Figure 4 , the second embodiment of the present invention provides a multi-scenario collaborative power dispatching operation strategy optimization system, including: a scenario scheduling module, and a data acquisition module and a scheduling optimization module connected thereto; Data acquisition module, used to obtain real-time power data and operating environment data of the power grid; The scenario scheduling module is used to analyze the operation scenario of the power grid based on the operation environment data; predict the power data of several links of the power grid based on the real-time power data of the power grid; and dispatch the power of the power grid according to the prediction results; The dispatch optimization module is used to optimize the dispatch operation of electricity according to the operation scenario of the power grid.

[0043] Some of the data in the above formula are calculated by removing the dimensions and taking their numerical values. The formula is a formula that is closest to the actual situation obtained by software simulation of a large amount of collected data; the preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained through simulation of a large amount of data.

[0044] The working principle of the present invention is as follows: the present invention obtains real-time power data and operating environment data of the power grid; analyzes the operating scenario of the power grid based on the operating environment data; predicts the power data of several links of the power grid based on the real-time power data of the power grid; dispatches the power of the power grid according to the prediction results; and optimizes the dispatching operation of power according to the operating scenario of the power grid.

[0045] The above embodiments are only used to illustrate the technical method of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A multi-scenario coordinated power dispatching operation strategy optimization method, characterized by: include: Obtain real-time power data and operating environment data of the power grid; Analyze the operation scenarios of the power grid based on the operating environment data; Predict power data of several links of the power grid based on real-time power data of the power grid; Dispatch power from the power grid based on the forecast results; Optimize the dispatching and operation of electricity according to the operating scenarios of the power grid.

2. The multi-scenario coordinated power dispatching operation strategy optimization method according to claim 1 is characterized in that: The analysis of the operation scenario of the power grid based on the operation environment data includes: Retrieve operating environment data; wherein the operating environment data includes: operating wind speed, operating air volume and light intensity; The wind energy coefficient of the operating environment is evaluated according to the operating wind speed and operating wind volume in the operating environment data; a plurality of scene types are obtained; the wind energy coefficient and the light intensity are matched with the plurality of scene types to obtain the operating scene of the power grid.

3. The multi-scenario coordinated power dispatching operation strategy optimization method according to claim 2 is characterized in that: The step of evaluating the wind energy coefficient of the operating environment according to the operating wind speed and the operating wind volume in the operating environment data includes: Retrieve the operating wind speed and operating air volume from the operating environment data; obtain air data; wherein the air data includes: temperature, air pressure, and water vapor pressure; Calculate the air density of air based on air data; by the formula Calculate the wind energy coefficient of the operating environment; where ρ represents air density, Q represents operating air volume, and v represents operating wind speed; The air density is calculated as: ; Where P represents air pressure, T represents temperature, e represents water vapor pressure; and R is the gas constant.

4. The multi-scenario coordinated power dispatching operation strategy optimization method according to claim 2 is characterized in that: The matching of wind energy coefficient and light intensity with several scene types includes: Retrieve the wind energy coefficient and light intensity; construct cluster centers for several scene types, and convert the wind energy coefficient and light intensity into environmental coordinates; where the wind energy coefficient is used as the horizontal coordinate and the light intensity is used as the vertical coordinate; Construct a distance analysis function: ; Where xf represents the value of the wind energy coefficient, yg represents the data of the light intensity; xi represents the wind energy coefficient corresponding to the i-th cluster center; yi represents the light intensity corresponding to the i-th cluster center; i=1, 2, ..., n, where n is a positive integer representing the number of cluster centers; The distance between the environment coordinates and several cluster centers is calculated according to the distance analysis function, and the one with the smallest distance is used as the scene type matched with the running environment data.

5. The multi-scenario coordinated power dispatching operation strategy optimization method according to claim 1 is characterized in that: The power data of several links of the power grid are predicted based on the real-time power data of the power grid, including: Retrieve real-time power data from the power grid, including real-time power generation, real-time carbon emissions, real-time market electricity prices, and real-time power consumption; Real-time power data of a set time period is sorted in chronological order to obtain a power analysis sequence; a power analysis model is called, and the power analysis sequence is input into the power analysis model to obtain a corresponding power label; the corresponding power data is matched according to the power label; wherein the power analysis model is constructed based on an artificial intelligence model; and the power label is set as a positive integer.

6. The multi-scenario coordinated power dispatching operation strategy optimization method according to claim 5 is characterized in that: The power analysis model is constructed based on an artificial intelligence model and includes: Selecting a suitable model and a deep learning framework from the artificial intelligence model; constructing the model based on the deep learning framework to obtain a constructed model; obtaining a standard data set; wherein the standard data set includes: standard input data consistent with the content attributes of the power analysis sequence, and standard output data consistent with the content attributes of the power label; The standard data set is divided into a training set, a validation set, and a test set according to a set ratio; the training set is used to train the construction model; the validation set is used to adjust the parameters of the construction model; the trained construction model is tested using the test set; when the test index is greater than the index threshold, the construction model is marked as a power analysis model; otherwise, the power analysis model is re-constructed and trained.

7. The multi-scenario coordinated power dispatching operation strategy optimization method according to claim 5 is characterized in that: The dispatching of power from the power grid according to the prediction result includes: Retrieving power data, including: predicted power generation, predicted power consumption, and predicted carbon emissions; comparing the predicted power generation with the predicted power consumption; When the predicted power consumption is less than the predicted power generation, the power generation is adjusted according to the difference between the two; otherwise, the power generation is increased according to the difference between the two; Retrieve the real-time market electricity price from the real-time power data of the power grid; adjust the power generation according to the real-time market electricity price and predicted carbon emissions.

8. The multi-scenario coordinated power dispatching operation strategy optimization method according to claim 7 is characterized in that: The adjustment of power generation according to the real-time market electricity price and predicted carbon emissions includes: Retrieve the real-time market electricity price and predicted electricity consumption of the power grid; calculate the electricity profit amount based on the real-time market electricity price and predicted electricity consumption; Retrieve predicted carbon emissions and obtain carbon prices; calculate power generation costs based on carbon emissions and carbon prices; analyze grid profits based on electricity profits and power generation costs; When the profit is greater than the profit threshold, the power generation is not adjusted; otherwise, the corresponding power generation is reduced according to the predicted power consumption.

9. The multi-scenario coordinated power dispatching operation strategy optimization method according to claim 4 is characterized in that: The optimization of power dispatching operation according to the operation scenario of the power grid includes: Retrieve the grid's operating scenario and analyze the operable power generation equipment based on the grid's operating scenario; power generation equipment includes wind power generation equipment, photovoltaic power generation equipment, and thermal power generation equipment; The wind energy coefficient and light intensity are retrieved, and the wind power generation of the wind power generation equipment is calculated according to the wind energy coefficient; the photovoltaic power generation of the photovoltaic power generation equipment is calculated according to the light intensity; and the power generation of the power grid is dispatched according to the wind power generation and photovoltaic power generation.

10. A multi-scenario collaborative power dispatching operation strategy optimization system, applied to the multi-scenario collaborative power dispatching operation strategy optimization method according to any one of claims 1 to 9, characterized in that: include: Scenario scheduling module, and the connected data acquisition module and scheduling optimization module; Data acquisition module, used to obtain real-time power data and operating environment data of the power grid; The scenario scheduling module is used to analyze the operation scenario of the power grid based on the operation environment data; predict the power data of several links of the power grid based on the real-time power data of the power grid; and dispatch the power of the power grid according to the prediction results; The dispatch optimization module is used to optimize the dispatch operation of electricity according to the operation scenario of the power grid.

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