Intelligent Scheduling Method and Device for Hybrid Energy System

Abstract

This application relates to the field of intelligent scheduling technology, specifically to an intelligent scheduling method and device for a hybrid energy system. The method includes: determining the power weights of various parameters at the current moment; determining the expected movement distance at the current moment, and combining with the target vector to determine the movement constraint vector at the current moment; based on the state change vector and the movement constraint vector, determining the weather feature vector at the current moment, and combining with the power weights of various parameters to determine the power fluctuation intensity at the current moment; based on the total climbing power and the power fluctuation intensity, completing the intelligent scheduling of the hybrid energy system. This application improves the voltage supply stability of the hybrid energy system by adjusting the sensitivity of the power grid control system to voltage deviation.

Description

Technical Field

[0001] This application relates to the technical field of intelligent scheduling, and specifically to an intelligent scheduling method and device for serving a hybrid energy system. Background Art

[0002] In a power system powered by hybrid energy, new energy power generation methods such as wind power and photovoltaic power are used. Such power generation methods are greatly affected by the weather and show large fluctuations. Therefore, the power system usually predicts the power generation of wind power and photovoltaic power according to the weather forecast, and formulates a power generation strategy in advance according to the power generation. At the same time, the power system will set fixed upper and lower voltage limits for power grid scheduling control. When the system voltage exceeds the upper and lower limits due to the instability of new energy equipment, the system will dispatch controllable power generation equipment according to the corresponding voltage overlimit situation to maintain voltage stability.

[0003] Among them, when the system voltage exceeds the limit, controllable power generation equipment is used to perform peak shaving and valley filling on the power output of the power grid. However, there is a problem with the ramp efficiency of controllable voltage equipment. When the power grid power fluctuates violently, the large difference between the upper and lower limit thresholds will cause the reaction scale of the power grid during scheduling, resulting in insufficient power output of the power grid and reducing the stability of the hybrid energy system's power supply voltage. Summary of the Invention

[0004] In order to solve the above technical problems, the purpose of this application is to provide an intelligent scheduling method and device for serving a hybrid energy system. The specific technical solutions adopted are as follows:

[0005] In a first aspect, an embodiment of this application provides an intelligent scheduling method for serving a hybrid energy system. The method includes the following steps:

[0006] For a preset duration before the current moment, obtain various types of actual and predicted weather data at each collection moment, as well as the actual and predicted output power of the power grid. The absolute value of the difference between each type of actual weather data and predicted weather data is denoted as each type of weather fluctuation data, and the absolute value of the difference between the actual output power and the predicted output power is denoted as power fluctuation data. Obtain the ramp power of all devices at the current moment and calculate the sum value, and the sum value is denoted as the total ramp power at the current moment;

[0007] Before the current moment, based on the average distribution of various types of weather fluctuation data and power fluctuation data at all collection moments, determine various types of relevant gain coefficients at the current moment, and combine the correlation between various types of weather fluctuation data and the power fluctuation data at all collection moments to determine various types of parameter power weights at the current moment;

[0008] Before the current moment, all weather fluctuation data at each acquisition moment form a weather feature vector. The absolute values of the differences between the corresponding elements in two weather feature vectors at the acquisition moments adjacent and next to the current moment are used to form the state change vector at the current moment, and the weather feature vector at the adjacent acquisition moment is used as the target vector at the current moment.

[0009] Taking all types of weather as the dimensionality of the coordinate system to construct a coordinate system, based on the distribution and dispersion of the distances from the coordinate origin to the projections of all weather feature vectors on the target vector, determine the motion expectation function at the current moment to determine the motion expectation distance at the current moment, and combine with the target vector to determine the motion constraint vector at the current moment; based on the state change vector and the motion constraint vector, determine the weather feature vector at the current moment, and combine with the power weight of each type of parameter to determine the power fluctuation intensity at the current moment.

[0010] Based on the total climbing power and the power fluctuation intensity, complete the intelligent scheduling of the hybrid energy system.

[0011] Preferably, the determination method of various relevant gain coefficients at the current moment is as follows:

[0012] Within a preset time period before the current moment, calculate the mean value of the power fluctuation data at all acquisition moments, which is denoted as the power fluctuation mean value at the current moment; calculate the mean value of various weather fluctuation data at all acquisition moments, which is denoted as the various weather fluctuation mean values at the current moment.

[0013] The various relevant gain coefficients at the current moment are the ratio of the power fluctuation mean value at the current moment to the various weather fluctuation mean values.

[0014] Preferably, the expression of the power weight of each type of parameter at the current moment is: A i = B i × C i ; In the formula, A i represents the power weight of the i-th type of parameter at the current moment; B i represents the i-th type of relevant gain coefficient at the current acquisition moment; C i represents the correlation between the power fluctuation data and the i-th type of weather fluctuation data at all acquisition moments within a preset time period before the current acquisition moment.

[0015] Preferably, the determination method of the motion expectation function at the current moment is as follows:

[0016] Calculate the mean value and standard deviation of the distances from the coordinate origin to the projections of all weather feature vectors on the target vector before the current moment, and use this mean value and standard deviation as the mean value and standard deviation in a one-dimensional Gaussian function to obtain the distribution probability function at the current moment.

[0017] The expression of the motion expectation function at the current moment is: g(x) = (X - x) × f(x); where g(x) represents the motion expectation function at the current moment; f(x) represents the distribution probability function at the current moment; X represents the distance from the target vector at the current moment to the origin of coordinates, and x represents the distance from the origin of coordinates to the projection of the weather feature vector at any acquisition moment on the target vector.

[0018] Preferably, the motion expectation distance at the current moment is the integral of the motion expectation function at the current moment over the interval [-3σ, 3σ], where σ represents the expectation of the motion expectation function at the current moment.

[0019] Preferably, the motion constraint vector at the current moment is the result of multiplying the motion expectation distance at the current moment by the unit vector of the target vector.

[0020] Preferably, the weather feature vector at the current moment is the sum vector of the state change vector at the current moment and the motion constraint vector.

[0021] Preferably, the method for determining the power fluctuation intensity at the current moment is as follows:

[0022] Calculate the product of the power weights of various parameters at the current moment and the corresponding elements in the weather feature vector, and take the sum of all products as the power fluctuation intensity at the current moment.

[0023] Preferably, the intelligent scheduling of the hybrid energy system includes:

[0024] If the power fluctuation intensity at the current moment is less than the total ramp power, set the upper and lower voltage threshold coefficients to preset values; otherwise, take the result of multiplying the ratio of the total ramp power to the power intensity by the preset value as the upper and lower voltage threshold coefficients;

[0025] Upper and lower voltage thresholds = preset standard voltage ± preset standard voltage × upper and lower voltage threshold coefficients;

[0026] If the voltage at the current moment exceeds the upper voltage threshold, reduce the power of the thermal power units in the power grid; if the voltage at the current moment is lower than the lower voltage threshold, increase the power of the thermal power units in the power grid; if the voltage at the current moment is between the upper and lower voltage thresholds, do not change the power of the thermal power units in the power grid.

[0027] In a second aspect, the embodiments of the present application also provide an intelligent scheduling device for a hybrid energy system, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of the intelligent scheduling method for a hybrid energy system described in any one of the above.

[0028] The present application has at least the following beneficial effects:

[0029] By analyzing the average distribution of various weather fluctuation data and the average distribution of power fluctuation data at all collection moments, this application determines various relevant gain coefficients at the current moment. Combining the correlation between various weather fluctuation data and the power fluctuation data at all collection moments, it determines various parameter power weights at the current moment. The beneficial effect is that it can judge the influence degree of different types of weather data on the grid voltage fluctuation and improve the accuracy of determining the upper and lower voltage limits. Based on the distribution and dispersion of the distances from the coordinate origin to the projections of all weather feature vectors on the target vector, this application determines the motion expectation function at the current moment to determine the motion expectation distance at the current moment, and combines with the target vector to determine the motion constraint vector at the current moment. Based on the state change vector and the motion constraint vector, it determines the weather feature vector at the current moment, and combines with the various parameter power weights to determine the power fluctuation intensity at the current moment. The beneficial effect is that it can avoid inaccurate setting of the upper and lower limit thresholds from affecting the grid dispatching and improve the stability of the power supply pressure of the power system. Based on the total climbing power and the power fluctuation intensity, this application adjusts the sensitivity of the grid control system to voltage offset, improving the power supply stability of the hybrid energy system. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following described drawings are only some embodiments of this application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0031] Figure 1 It is a flowchart of the steps of an intelligent dispatching method for a hybrid energy system provided by an embodiment of this application;

[0032] Figure 2 It is a flowchart of the steps for obtaining various parameter power weights provided by an embodiment of this application;

[0033] Figure 3 It is a schematic diagram of the power fluctuation intensity extraction process provided by an embodiment of this application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0034] In order to further elaborate on the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the following will, in conjunction with the accompanying drawings and preferred embodiments, elaborate in detail on the specific implementation manner, structure, features and effects of the intelligent scheduling method and device for a hybrid energy system proposed according to the present application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0035] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which this application belongs.

[0036] The following will specifically describe the specific solutions of the intelligent scheduling method and device for a hybrid energy system provided by the present application in conjunction with the accompanying drawings.

[0037] Please refer to Figure 1 , which shows a flowchart of the steps of an intelligent scheduling method for a hybrid energy system provided by an embodiment of the present application. The method includes the following steps:

[0038] Step S1: For a preset duration before the current moment, obtain various actual and predicted weather data at each acquisition moment, as well as the actual and predicted output power of the power grid; obtain the ramp power of all devices at the current moment.

[0039] Through the power grid control system, obtain various actual and predicted weather data at each acquisition moment within a preset duration t before the current moment, as well as the actual and predicted output power of the power grid. Set the sampling interval as T. The absolute value of the difference between each type of actual weather data and the predicted weather data at each acquisition moment is denoted as the weather fluctuation data of each type at each acquisition moment. The absolute value of the difference between the actual output power and the predicted output power at each acquisition moment is denoted as the power fluctuation data at each acquisition moment. Among them, the various weather data include wind speed, irradiance, temperature, humidity and cloud cover.

[0040] It should be noted that the values of the preset duration t and the sampling interval T are both set artificially. In this embodiment, the value of the preset duration t is 1h, and the value of the sampling interval T is 1s. Implementers can also set them according to specific situations by themselves, and this embodiment does not make special restrictions.

[0041] Collect the ramp power of all devices at the current moment through the power grid control system, and denote the sum value of the total ramp power of all devices as the total ramp power at the current moment.

[0042] Further, in order to eliminate the influence of data dimension and unit, all weather fluctuation data, power fluctuation data, and total ramping power are normalized for all weather conditions. Among them, there are many common normalization methods. In this embodiment, the z-score normalization method is used to process the data. Implementers can also use the maximum-minimum normalization method to normalize the data. There is no special limitation on the selection of the normalization method in this embodiment. The z-score normalization method is a well-known technology, and the specific process of normalizing the data will not be elaborated here.

[0043] Step S2: Before the current moment, based on the average distribution of various weather fluctuation data and power fluctuation data at all acquisition moments, determine the various relevant gain coefficients at the current moment. Combining the correlation between various weather fluctuation data and the power fluctuation data at all acquisition moments, determine the various parameter power weights at the current moment.

[0044] Among the obtained weather data, some weather data has a more direct impact on new energy power generation equipment. Its instantaneous change has a greater impact on the power of new energy equipment, and it brings greater voltage fluctuation pressure to the power grid voltage.

[0045] Therefore, when dynamically adjusting the upper and lower limits of the power grid voltage through weather data, it is necessary to consider the different fluctuation impacts brought by different types of weather data on the power grid voltage. By analyzing the average distribution of power fluctuation data and various weather fluctuation data at all acquisition moments before the current moment, determine the various relevant gain coefficients at the current moment. Combining the correlation between various weather fluctuation data and the power fluctuation data at all acquisition moments, determine the various parameter power weights at the current moment to judge the influence degree of different types of weather data on the power grid voltage fluctuation, so as to improve the accuracy of determining the upper and lower limits of the voltage. Specifically:

[0046] (1) Within a preset time period before the current moment, calculate the mean value of the power fluctuation data at all acquisition moments, denoted as the power fluctuation mean value at the current moment; calculate the mean value of various weather fluctuation data at all acquisition moments, denoted as the various weather fluctuation mean values at the current moment;

[0047] (2) Further, take the ratio of the power fluctuation mean value at the current moment to the various weather fluctuation mean values as the various relevant gain coefficients at the current moment;

[0048] In particular, when there is no fluctuation in the weather data, that is, when the mean value of the weather fluctuation data is 0, it means that the weather change has no impact on the power grid power fluctuation, and the relevant gain coefficient is 0.

[0049] (3) Further, analyze the correlation between various weather fluctuation data and the power fluctuation data at all acquisition moments within a preset time period before the current acquisition moment;

[0050] It should be noted that there are many methods to measure the correlation between data groups. In this embodiment, the Pearson correlation coefficient between all types of weather fluctuation data at adjacent acquisition times before the current acquisition time and the power fluctuation data at all acquisition times is calculated to measure the correlation between the power fluctuation data and various types of weather fluctuation data. Implementers can also use other methods to measure the correlation between data groups, such as the Spearman correlation coefficient or the Kendall rank correlation coefficient. This embodiment does not make special restrictions on the selection of methods for measuring the correlation between data groups.

[0051] Among them, the calculation process of the Pearson correlation coefficient is a well-known technology, and its specific calculation steps will not be elaborated here.

[0052] (4) Further, based on the correlation and the various types of correlation gain coefficients, determine the power weights of various parameters at the current moment. Specifically:

[0053] The power weight A i of the i-th type of parameter at the current moment is expressed as: A i = B i × C i ; In the formula, B i represents the i-th type of correlation gain coefficient at the current acquisition time; C i represents the correlation between the power fluctuation data at all acquisition times within a preset time period before the current acquisition time and the i-th type of weather fluctuation data.

[0054] Further, it can be understood from the power weights of various parameters at the current moment that the greater the correlation gain coefficient at the current moment and the higher the correlation between the power fluctuation data and the weather fluctuation data, the greater the impact of weather changes on the power of the power grid, and the greater the power weight of the parameter; on the contrary, the smaller the correlation gain coefficient at the current moment and the lower the correlation between the power fluctuation data and the weather fluctuation data, the smaller the impact of weather changes on the power of the power grid, and the smaller the power weight of the parameter.

[0055] Preferably, the flowchart of the steps for obtaining the power weights of various parameters provided in this embodiment is as Figure 2 shown.

[0056] Step S3: Construct a coordinate system with all types of weather as the coordinate system dimensions. Based on the distribution and dispersion of the distances from the coordinate origin to the projections of all weather feature vectors on the target vector, determine the motion expectation function at the current moment to determine the motion expectation distance at the current moment, and combine the target vector to determine the motion constraint vector at the current moment; based on the state change vector and the motion constraint vector, determine the weather feature vector at the current moment, and combine the power weights of various parameters to determine the power fluctuation intensity at the current moment.

[0057] The weather system is a chaotic system, which refers to a system that cannot be accurately predicted due to insufficient prior conditions, but can be partially predicted. A chaotic system usually describes the characteristics of a system as a multidimensional coordinate system, and any coordinate point in the coordinate system represents a state of the system. The system state will fluctuate over time and cannot be accurately predicted. In the coordinate system, it is manifested as a coordinate point moving randomly over time. Although the system state of a chaotic system cannot be predicted, its movement is usually constrained within a certain range, and its random movement has a certain probability.

[0058] Therefore, in this embodiment, a coordinate system is constructed with various types of weather as coordinate axes, and each coordinate axis represents a type of weather. As time changes, the weather state fluctuates around the coordinate origin. The more severe the weather fluctuation, the greater the impact on the voltage and power in the power grid. Therefore, based on the distribution and discreteness of the distance from the coordinate origin to the projection of all weather characteristic vectors on the target vector, the expected movement distance at the current moment is determined, and the movement constraint vector at the current moment is determined in combination with the target vector; the weather characteristic vector at the current moment is determined based on the state change vector and the movement constraint vector, and the power fluctuation intensity at the current moment is determined in combination with the power weights of the various parameters, and the severity of the electric energy fluctuation in the power grid is judged to avoid inaccurate upper and lower limit threshold settings that affect the dispatch of the power grid, and improve the stability of the power supply voltage of the power system. Specifically:

[0059] (1) Before the current moment, all types of weather fluctuation data at each collection moment constitute a weather feature vector. The absolute value of the difference between the corresponding elements in the two weather feature vectors at the adjacent and next adjacent collection moments to the current moment constitutes the state change vector at the current moment. The weather feature vector at the adjacent collection moment is used as the target vector at the current moment.

[0060] (2) Further, the mean and standard deviation of the distance from the coordinate origin to the projection of all weather feature vectors on the target vector before the current moment are calculated, and the mean and standard deviation are used as the mean and standard deviation in the one-dimensional Gaussian function to obtain the distribution probability function at the current moment;

[0061] The expression of the motion expectation function at the current moment is: g(x)=(Xx)×f(x); where g(x) represents the motion expectation function at the current moment; f(x) represents the distribution probability function at the current moment; X represents the distance from the target vector to the origin of the coordinate system at the current moment, and x represents the distance from the origin of the coordinate system to the projection of the weather feature vector at any acquisition moment on the target vector.

[0062] Among them, the one-dimensional Gaussian function is a well-known technology, and the specific principle will not be repeated here.

[0063] (3) Further, integrate the motion expectation function at the current moment over the interval [-3σ, 3σ], and take the integral result as the motion expectation distance at the current moment, where σ represents the expectation of the motion expectation function at the current moment.

[0064] Among them, the 3σ principle, obtaining the expectation of a function, and calculating the area under the curve of a function are all common techniques in the field of data processing. Their specific principles and calculation processes will not be elaborated here.

[0065] (4) Further, the result of multiplying the motion expectation distance at the current moment by the unit vector of the target vector is used as the motion constraint vector at the current moment; the sum vector of the state change vector and the motion constraint vector at the current moment is used as the weather feature vector at the current moment.

[0066] (5) Calculate the product of the power weight of each type of parameter at the current moment and the corresponding element in the weather feature vector, and take the sum of all products as the power fluctuation intensity at the current moment.

[0067] Further, it can be understood from the power fluctuation intensity at the current moment that the more drastic the weather change, the greater the value of the parameter power weight, and the greater the element value in the weather feature vector, the greater the power fluctuation intensity, indicating that the weather change has too much influence on the power fluctuation. At the current moment, a smaller upper and lower limit voltage of the power grid should be used to control the hybrid energy system to make the power grid control more sensitive and avoid problems caused by the ramp efficiency of the power grid; on the contrary, the gentler the weather change, the smaller the value of the parameter power weight, and the smaller the element value in the weather feature vector, the smaller the power fluctuation intensity, indicating that the weather change has less influence on the power fluctuation, and the upper and lower limit voltages can be appropriately increased. Preferably, the schematic diagram of the power fluctuation intensity extraction process provided in this embodiment is as Figure 3 shown.

[0068] Step S4: Based on the total ramp power and power fluctuation intensity at the current moment, complete the intelligent scheduling of the hybrid energy system.

[0069] By adjusting the upper and lower limit voltage threshold coefficients of the hybrid energy power grid to improve the power supply stability effect of the hybrid energy power grid, it is necessary to establish a connection between the power fluctuation intensity and the grid voltage. In this embodiment, the power fluctuation intensity and the total ramp power are combined to establish a connection between power and voltage to obtain the upper and lower limit voltage threshold coefficients. The specific method is as follows:

[0070] If the power fluctuation intensity at the current moment is less than the total ramp power, set the upper and lower limit voltage threshold coefficient to a preset value; otherwise, take the result of multiplying the ratio of the total ramp power to the power intensity by the preset value as the upper and lower limit voltage threshold coefficient. In particular, when the product is less than 3%, take 3% as the threshold coefficient.

[0071] It should be noted that in this embodiment, the threshold coefficient of the fixed upper and lower limit voltages of the power grid is L = 10%, and the preset standard voltage is taken as 220 kV, which means that the voltage is within the range of ±10% of the voltage stability value, that is, the fixed upper limit voltage is 242 kV and the lower limit voltage is 198 kV.

[0072] Among them, when the power fluctuation intensity is large, the upper and lower limit voltage threshold coefficients are reduced to improve the sensitivity of power grid dispatching and avoid power grid instability caused by the ramp efficiency problem; at the same time, the minimum upper and lower limit voltage threshold coefficients are set to avoid the power grid system being too sensitive, resulting in frequent switching of equipment and causing equipment loss.

[0073] Upper and lower limit voltages threshold = preset standard voltage ± preset standard voltage × upper and lower limit voltage threshold coefficient;

[0074] If the voltage at the current moment exceeds the upper limit voltage threshold, the power of the thermal power units in the power grid is reduced; if the voltage at the current moment is lower than the lower limit voltage threshold, the power of the thermal power units in the power grid is increased; if the voltage at the current moment is between the upper and lower limit voltage thresholds, the power of the thermal power units in the power grid remains unchanged.

[0075] Based on the same inventive concept as the above method, the embodiment of the present application also provides an intelligent dispatching device for a hybrid energy system, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above-mentioned intelligent dispatching methods for a hybrid energy system.

[0076] It should be noted that: the above sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above description of specific embodiments of this specification is given. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0077] Each embodiment in this specification is described in a progressive manner. The same or similar parts among the embodiments can be referred to each other, and the key points of each embodiment are to illustrate the differences from other embodiments.

[0078] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the principle of the present application shall be included in the protection scope of the present application.

Claims

1. An intelligent dispatching method for a hybrid energy system, characterized in that: The method comprises the following steps: For a preset time period before the current moment, multiple types of actual and predicted weather data at each collection moment, as well as the actual and predicted output power of the power grid, are obtained. The absolute value of the difference between each type of actual weather data and the predicted weather data is recorded as each type of weather fluctuation data. The absolute value of the difference between the actual output power and the predicted output power is recorded as the power fluctuation data. The climbing power of all devices at the current moment is obtained and the sum is calculated. The sum is recorded as the total climbing power at the current moment. Before the current moment, based on the average distribution of various weather fluctuation data and power fluctuation data at all collection moments, various related gain coefficients at the current moment are determined, and combined with the correlation between various weather fluctuation data and power fluctuation data at all collection moments, various parameter power weights at the current moment are determined; Before the current moment, all weather fluctuation data at each collection moment form a weather feature vector. The absolute value of the difference between the corresponding elements in the two weather feature vectors at the collection moments adjacent to and next to the current moment forms the state change vector at the current moment. The weather feature vector at the adjacent collection moment is used as the target vector at the current moment. Construct a coordinate system with all types of weather as the coordinate system dimension, determine the motion expectation function at the current moment based on the distribution and discreteness of the distance from the coordinate origin to the projection of all weather characteristic vectors on the target vector, so as to determine the motion expectation distance at the current moment, and determine the motion constraint vector at the current moment in combination with the target vector; determine the weather characteristic vector at the current moment based on the state change vector and the motion constraint vector, and determine the power fluctuation intensity at the current moment in combination with the power weights of the various parameters; Based on the total climbing power and the power fluctuation intensity, intelligent scheduling of the hybrid energy system is completed; The method for determining the motion expectation function at the current moment is: Calculate the mean and standard deviation of the distance from the coordinate origin to the projection of all weather feature vectors on the target vector before the current moment, use the mean and standard deviation as the mean and standard deviation in the one-dimensional Gaussian function, and obtain the distribution probability function at the current moment; The expression of the motion expectation function at the current moment is: g(x)=(Xx)×f(x); where g(x) represents the motion expectation function at the current moment; f(x) represents the distribution probability function at the current moment; X represents the distance from the target vector to the origin of the coordinate system at the current moment, and x represents the distance from the origin of the coordinate system to the projection of the weather feature vector at any acquisition moment on the target vector.

2. The intelligent dispatching method for serving a hybrid energy system according to claim 1, characterized in that: The method for determining various types of related gain coefficients at the current moment is: Calculate the mean of the power fluctuation data at all collection times within the preset time before the current moment, and record it as the mean power fluctuation at the current moment; calculate the mean of various weather fluctuation data at all collection times, and record it as the mean weather fluctuation at the current moment; The various related gain coefficients at the current moment are the ratios of the mean power fluctuation at the current moment to the mean weather fluctuations of various types.

3. The intelligent dispatching method for serving a hybrid energy system according to claim 1, characterized in that: The expression of the power weight of various parameters at the current moment is: i =B i ×C i Where A i represents the power weight of the i-th parameter at the current moment; B i represents the i-th type correlation gain coefficient at the current acquisition moment; C i It represents the correlation between the power fluctuation data of all collection moments within a preset time period before the current collection moment and the i-th type of weather fluctuation data.

4. The intelligent dispatching method for serving a hybrid energy system according to claim 1, characterized in that: The expected motion distance at the current moment is the integral of the expected motion function at the current moment over the interval [-3σ, 3σ], where σ represents the expectation of the expected motion function at the current moment.

5. The intelligent dispatching method for serving a hybrid energy system according to claim 1, characterized in that: The motion constraint vector at the current moment is the result of multiplying the expected motion distance at the current moment by the unit vector of the target vector.

6. The intelligent dispatching method for serving a hybrid energy system according to claim 1, characterized in that: The weather characteristic vector at the current moment is the sum vector of the state change vector and the motion constraint vector at the current moment.

7. The intelligent dispatching method for serving a hybrid energy system according to claim 1, characterized in that: The method for determining the power fluctuation intensity at the current moment is: Calculate the product of the power weights of various parameters at the current moment and the corresponding class elements in the weather feature vector, and take the cumulative sum of all products as the power fluctuation intensity at the current moment.

8. The intelligent dispatching method for serving a hybrid energy system according to claim 1, characterized in that: The intelligent scheduling of the hybrid energy system includes: If the power fluctuation intensity at the current moment is less than the total climbing power, the upper and lower voltage threshold coefficients are set to preset values; otherwise, the result of multiplying the ratio of the total climbing power to the power intensity by the preset value is used as the upper and lower voltage threshold coefficients; Upper and lower voltage thresholds = preset standard voltage ± preset standard voltage × upper and lower voltage threshold coefficients; If the voltage at the current moment exceeds the upper voltage threshold, the power of the thermal power units in the power grid will be reduced. If the voltage at the current moment is lower than the lower voltage threshold, the power of the thermal power units in the power grid will be increased. If the voltage at the current moment is between the upper and lower voltage thresholds, the power of the thermal power units in the power grid will not be changed.

9. An intelligent dispatching device serving a hybrid energy system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the intelligent scheduling method serving the hybrid energy system as described in any one of claims 1-8 are implemented.

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