Power grid frequency regulation method, system, equipment, product and medium based on probability density

Abstract

The present invention relates to the field of energy management technology, and provides a probability density-based power grid frequency regulation method, system, equipment, product, and medium. The method comprises calculating a system load deviation component, a tie line deviation component, and a unit deviation component through operating data; obtaining a new energy output prediction model to predict new energy power generation equipment, calculating a probability density function to obtain a new energy prediction deviation, and thus calculating a new energy deviation component; calculating the net load power and the net load power standard deviation of a target power grid, inputting multiple parameters into a deep neural network, obtaining a deviation prediction result, and calculating a loss function; iterating the loss function to obtain a target loss function, and extracting a target prediction value from the target loss function; establishing an evaluation parameter screening condition, calculating an evaluation parameter, iterating the target prediction value, obtaining a prediction result, and performing frequency regulation on the target power grid. The present invention improves the efficiency of power grid frequency regulation.

Description

Technical Field

[0001] The present invention relates to the field of energy management technology, and in particular to a method, system, equipment, product and medium for power grid frequency modulation based on probability density. Background Art

[0002] With the development of new power systems, renewable energy sources such as wind power and photovoltaics are increasingly being integrated into the grid on a large scale. The randomness and volatility of their generation severely impacts the stability of power system frequency. To cope with the large-scale integration of wind power, photovoltaics, and other renewable energy sources, power systems need to provide sufficient AGC (Automatic Generation Control) frequency regulation capacity. However, excessive frequency regulation capacity cannot meet the economic needs of system operation. Rationally predicting the AGC frequency regulation capacity required for system operation is crucial for grid frequency stability and system economic operation.

[0003] For regional power grids, AGC frequency regulation capacity is primarily used to balance minute-by-minute power fluctuations within the region. Using regional control deviation as a reference, it balances the power difference between actual power load and power generation output within the region. Currently, the main methods for calculating power system frequency regulation capacity include percentage-based frequency regulation capacity calculations. These methods rely primarily on dispatching and operational experience, determining the system's frequency regulation capacity requirements as a percentage of the power system's peak load. For example, the Beijing-Tianjin-Tangshan control zone of the North China Power Grid determines frequency regulation capacity requirements based on 10% of the weekly forecasted maximum load. The Shanxi Power Grid uses 5% to 15% of the maximum daily direct dispatch power generation demand as the system's frequency regulation capacity requirement. While simple and practical, these methods rely heavily on dispatching experience and fail to account for the volatility of renewable energy generation such as wind power and photovoltaics, resulting in significant errors.

[0004] Frequency regulation capacity calculation methods based on load deviation coverage. This type of method considers the volatility of load and unit output and calculates future frequency regulation capacity based on operational data. While this method accounts for the impact of system power fluctuations, it relies solely on operational data and lacks consideration of the actual grid conditions, making it less applicable. Summary of the Invention

[0005] The present invention aims to solve at least one of the technical problems existing in the related art. To this end, the present invention provides a probability density-based power grid frequency regulation method, system, device, product, and medium to achieve high-confidence prediction of the frequency regulation capacity of the power grid frequency regulation system.

[0006] The present invention provides a power grid frequency modulation method based on probability density, comprising:

[0007] S1: Acquire operating data of a target power grid, and calculate a system load deviation component, a tie line deviation component, and a unit deviation component of the target power grid based on the operating data;

[0008] S2: Obtain a new energy output prediction model, predict the new energy power generation equipment in the target power grid using the new energy output prediction model to obtain a prediction result, construct a probability density function of the prediction result and obtain a new energy prediction deviation of the new energy power generation equipment, and calculate a new energy deviation component using the new energy prediction deviation;

[0009] S3: Calculate the net load power and the net load power standard deviation of the target power grid, obtain a deep neural network, input the system load deviation component, the tie line deviation component, the unit deviation component, the new energy deviation component, the net load power and the net load power standard deviation into the deep neural network, obtain a deviation prediction result, and calculate a loss function for the deviation prediction result;

[0010] S4: Iterating the loss function to obtain a target loss function, and extracting a target prediction value from the target loss function;

[0011] S5: Establishing evaluation parameter screening conditions, calculating the evaluation parameters of the target prediction value, screening the target prediction value using the evaluation parameters and the evaluation parameter screening conditions to obtain a prediction result, and frequency-modulating the target power grid using the prediction result.

[0012] According to the probability density-based power grid frequency modulation method provided by the present invention, step S1 further includes:

[0013] S11: determining the target power grid, obtaining the operating data of the target power grid, calculating the system load forecast deviation and the system load fluctuation deviation of the target power grid based on the operating data, and calculating the system load deviation component based on the system load forecast deviation and the system load fluctuation deviation;

[0014] S12: acquiring a planned exchange value after a period of time and a planned exchange value in the current period of time of the tie line from the operation data, and obtaining the tie line deviation component by using the planned exchange value after a period of time and the planned exchange value in the current period of time;

[0015] S13: Obtaining a planned output value after a period of time and a planned output value for the current period of time of the generator set from the operation data, and calculating the generator set deviation component by using the planned output value after a period of time and the planned output value for the current period of time.

[0016] According to the probability density-based power grid frequency modulation method provided by the present invention, S21: determining a prediction time and a new energy power generation device in the target power grid, obtaining a new energy output prediction model, and performing multiple predictions on the power generation power of the new energy power generation device based on the prediction time to obtain a prediction result;

[0017] S22: determining a confidence interval of the prediction result, constructing a probability density function of the prediction result according to the probability distribution of the prediction result, and obtaining the new energy prediction deviation according to the confidence interval and the probability density function;

[0018] S23: Obtaining a new energy output prediction value of the new energy power generation equipment through a probability density function of the prediction result, and obtaining the new energy deviation component through the new energy prediction deviation and the new energy output prediction value.

[0019] According to the probability density-based power grid frequency modulation method provided by the present invention, step S3 further includes:

[0020] S31: Calculating the net load power and the net load power standard deviation of the target power grid according to the new energy output forecast value, the power grid load forecast value, the planned output value for this period, and the planned exchange value for this period;

[0021] S32: Obtain a deep neural network, input the system load deviation component, the tie line deviation component, the unit deviation component, the new energy deviation component, the net load power, and the net load power standard deviation into the fully connected layer and activation function layer of the deep neural network to obtain the deviation prediction result;

[0022] S33: Construct a likelihood function of the deviation prediction result, and take the logarithm of the likelihood function to obtain the loss function of the deviation prediction result.

[0023] According to the probability density-based power grid frequency regulation method provided by the present invention, when iterating the loss function, an optimizer is obtained, and the parameters of the deep neural network are adjusted by the optimizer. A plurality of the deviation prediction results are obtained by the deep neural network, and the loss function of each of the deviation prediction results is calculated. A loss function with the minimum loss is found in the loss function of each of the deviation prediction results, and the loss function is used as the target loss function.

[0024] According to the probability density-based power grid frequency modulation method provided by the present invention, in step S5, when the evaluation parameter meets the evaluation parameter screening condition, the target prediction value is used as the prediction result;

[0025] When the evaluation parameter does not meet the evaluation parameter screening condition, the confidence interval of the prediction result is changed and the target prediction value is recalculated until the target prediction value meets the evaluation parameter screening condition, and the target prediction value at this time is used as the prediction result.

[0026] The present invention also provides a power grid frequency regulation system based on probability density, comprising:

[0027] Deviation component module: used to obtain the operating data of the target power grid, and calculate the system load deviation component, tie line deviation component and unit deviation component of the target power grid based on the operating data;

[0028] New energy deviation component module: used to obtain a new energy output prediction model, predict the new energy power generation equipment in the target power grid using the new energy output prediction model to obtain a prediction result, construct a probability density function of the prediction result and obtain the new energy prediction deviation of the new energy power generation equipment, and calculate the new energy deviation component based on the new energy prediction deviation;

[0029] Loss function module: used to calculate the net load power and net load power standard deviation of the target power grid, obtain a deep neural network, input the system load deviation component, the tie line deviation component, the unit deviation component, the new energy deviation component, the net load power and the net load power standard deviation into the deep neural network, obtain a deviation prediction result, and calculate a loss function for the deviation prediction result;

[0030] Target prediction value module: used for iterating the loss function to obtain a target loss function, and extracting a target prediction value from the target loss function;

[0031] The power grid frequency modulation module is used to establish evaluation parameter screening conditions, calculate the evaluation parameters of the target prediction value, filter the target prediction value according to the evaluation parameters and the evaluation parameter screening conditions, obtain the prediction result, and modulate the frequency of the target power grid according to the prediction result.

[0032] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of any of the above-described probability density-based grid frequency modulation methods are implemented.

[0033] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of any of the above-mentioned probability density-based power grid frequency regulation methods are implemented.

[0034] The present invention also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer can perform the steps of any of the probability density-based power grid frequency regulation methods described above.

[0035] The above one or more technical solutions in the embodiments of the present invention have at least one of the following technical effects:

[0036] The probability density-based grid frequency regulation method, system, device, product, and medium provided by the present invention comprehensively consider the impact of system load, renewable energy, tie lines, generator sets, and system net load on AGC frequency regulation capacity. This avoids the over-reliance on dispatching and operating experience or the single factor of load fluctuation in traditional frequency regulation capacity calculations, and is more adaptable to the frequency regulation capacity requirements of new power systems in environments with a high proportion of renewable energy. Furthermore, the present invention combines a probability density function with a deep neural network, and evaluates the target prediction value in conjunction with a first evaluation parameter and a second evaluation parameter to ensure that the final prediction result meets the requirements, thereby providing the power grid with a more accurate frequency regulation capacity prediction result, and frequency regulation of the power grid is performed based on the frequency regulation capacity prediction result.

[0037] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned by practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the present invention or 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 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.

[0039] Figure 1 It is a flow chart of the probability density-based power grid frequency modulation method provided by the present invention.

[0040] Figure 2 It is a structural diagram of the power grid frequency modulation system based on probability density provided by the present invention.

[0041] Figure 3 It is a structural diagram of the power grid frequency modulation equipment based on probability density provided by the present invention.

[0042] Reference numerals:

[0043] 100, deviation component module; 200, new energy deviation component module; 300, loss function module; 400, target prediction value module; 500, grid frequency regulation module; 810, processor; 820, communication interface; 830, memory; 840, communication bus. DETAILED DESCRIPTION

[0044] To make the purpose, technical solutions and advantages of the present invention clearer, the technical solutions in the present invention will be clearly and completely described below. Obviously, the embodiments described are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. The following embodiments are used to illustrate the present invention, but are not used to limit the scope of the present invention.

[0045] In the description of the embodiments of the present invention, it should be noted that the terms “first”, “second” and “third” are used for descriptive purposes only and should not be understood as indicating or implying relative importance.

[0046] In the description of the embodiments of the present invention, it should be noted that, unless otherwise specified or limited, the terms "connected" and "connection" should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integral connections; mechanical connections, electrical connections; and direct connections or indirect connections through an intermediary. Those skilled in the art will understand the specific meanings of the above terms in the embodiments of the present invention based on the specific circumstances.

[0047] In the description of this specification, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the embodiment of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0048] The following combination Figures 1 to 3 Describe embodiments of the present invention,

[0049] Figure 1 This is a flow chart of the power grid frequency regulation method based on probability density. As shown in the figure, the operating data of the target power grid is first obtained, and then the deviation components of the system load, interconnection line, and unit are calculated. The new energy deviation component is obtained through the new energy output prediction model, and then the loss function is obtained through the deep neural network. The loss function is iterated and the target prediction value is obtained. Finally, the first evaluation parameter and the second evaluation parameter are calculated and it is judged whether they meet the evaluation parameter screening conditions. If they meet the conditions, the target result is obtained and the target power grid frequency is regulated. Otherwise, the confidence interval needs to be changed and the new energy deviation component needs to be recalculated.

[0050] The present invention provides a power grid frequency modulation method based on probability density, comprising:

[0051] S1: Acquire operating data of a target power grid, and calculate a system load deviation component, a tie line deviation component, and a unit deviation component of the target power grid based on the operating data;

[0052] Furthermore, the purpose of this stage is to calculate the system load deviation component, tie line deviation component and unit deviation component of the target power grid based on the operating data of the target power grid, thereby providing data for subsequent deep neural network prediction. Among them, step S1 further includes:

[0053] S11: determining the target power grid, obtaining the operating data of the target power grid, calculating the system load forecast deviation and the system load fluctuation deviation of the target power grid based on the operating data, and calculating the system load deviation component based on the system load forecast deviation and the system load fluctuation deviation;

[0054] S12: acquiring a planned exchange value after a period of time and a planned exchange value in the current period of time of the tie line from the operation data, and obtaining the tie line deviation component by using the planned exchange value after a period of time and the planned exchange value in the current period of time;

[0055] S13: Obtaining a planned output value after a period of time and a planned output value for the current period of time of the generator set from the operation data, and calculating the generator set deviation component by using the planned output value after a period of time and the planned output value for the current period of time.

[0056] Regarding the above steps, the specific implementation methods in this embodiment are as follows:

[0057] First, determine the target power grid that needs frequency regulation and obtain the target power grid's operating data. The operating data includes the target power grid's current and past operating data obtained by the target power grid's dispatching system, as well as the dispatching system's prediction of the target power grid's load level, generator set power generation, and tie-line exchange volume in the future. Then, calculate the target power grid's system load forecast deviation in the i-th period based on the operating data. and the system load fluctuation deviation of the target power grid in the i-th period :

[0058]

[0059]

[0060] Wherein, avg() means to find the average value of the values ​​in the brackets. In this embodiment, it is divided by the length of the i-th period. represents the planned load value of the power grid in the i-th period, represents the grid load forecast value for the i-th period, represents the maximum value of the grid load in the i-th period in the operating data, It represents the minimum value of the grid load in the i-th period in the operating data. The above data are all included in the operating data.

[0061] Then the system load deviation component is calculated by the system load forecast deviation and the system load fluctuation deviation. :

[0062] .

[0063] Then get the i-th tie line from the operation data The post-period planned exchange value for the period and the planned exchange value of the i-th period , the tie line deviation component of the i-th period is obtained by the planned exchange value after the period and the planned exchange value of the current period :

[0064]

[0065] Here, since the target grid is connected to other grids, other grids will also exchange energy with the target grid, and the energy exchange between grids is carried out through the tie line. If the target grid is an isolated system, the tie line deviation component is 0.

[0066] Finally, the i-th generator set is obtained from the operating data. The planned output value after each period and the planned output value of the i-th period , thus calculating the group deviation component :

[0067] .

[0068] S2: Obtain a new energy output prediction model, predict the new energy power generation equipment in the target power grid using the new energy output prediction model to obtain a prediction result, construct a probability density function of the prediction result and obtain a new energy prediction deviation of the new energy power generation equipment, and calculate a new energy deviation component using the new energy prediction deviation;

[0069] Furthermore, the purpose of this stage is to obtain a new energy output prediction model and predict the new energy power generation equipment using the new energy output model to obtain a prediction structure and calculate the new energy prediction deviation, and finally calculate the new energy deviation component, wherein step S2 further includes:

[0070] S21: Determine the prediction time and the new energy power generation equipment in the target power grid, obtain a new energy output prediction model, and perform multiple predictions on the power generation of the new energy power generation equipment according to the prediction time to obtain a prediction result.

[0071] S22: determining a confidence interval of the prediction result, constructing a probability density function of the prediction result according to the probability distribution of the prediction result, and obtaining the new energy prediction deviation according to the confidence interval and the probability density function;

[0072] S23: Obtaining a new energy output prediction value of the new energy power generation equipment through a probability density function of the prediction result, and obtaining the new energy deviation component through the new energy prediction deviation and the new energy output prediction value.

[0073] Regarding the above steps, the specific implementation methods in this embodiment are as follows:

[0074] First, determine the prediction time. In this embodiment, the prediction time is the i-th period and the i-th period. time periods. It is also necessary to determine the new energy power generation equipment in the target power grid. In this embodiment, the new energy power generation equipment is solar power generation equipment and wind power generation equipment. Since the power generation capacity of new energy power generation equipment is greatly affected by external factors such as weather, it is necessary to obtain a new energy output prediction model. The new energy output prediction model can obtain data on external factors such as weather, and make multiple predictions on the power generation power of the new energy power generation equipment based on the data of these external factors and the status of the new energy power generation equipment, thereby obtaining a prediction result. Due to the characteristics of the new energy output prediction model, the prediction results output by the new energy output prediction model will present a probability distribution, that is, it can output multiple results and there is a probability of outputting a certain result among these results, so the prediction result also presents a probability distribution.

[0075] Then, the confidence interval of the prediction result is determined. In this embodiment, the confidence interval is selected as 90%. At the same time, the probability density function of the prediction result can be constructed according to the probability distribution of the prediction structure. The new energy prediction deviation can be obtained through the confidence interval and probability density function. The new energy prediction deviation includes the wind power prediction deviation value of the i-th period under the 90% confidence interval. The photovoltaic forecast deviation value of the i-th period under the 90% confidence interval .

[0076] Then, the result with the highest probability in the probability density function of the prediction results is taken as the new energy output prediction value of the new energy power generation equipment, including the photovoltaic output prediction value of the i-th period , i-th PV output forecast for each period , wind power output forecast value in the i-th period and the i Wind power output forecast value for each period .

[0077] Then, the photovoltaic output forecast value of the i-th period and the i-th period Calculate the photovoltaic fluctuation deviation of the ith period based on the photovoltaic output forecast value of the ith period :

[0078]

[0079] Similarly, through the wind power output forecast value of the i-th period and the i-th period The wind power output forecast value of each period is used to calculate the wind power fluctuation deviation of the i-th period :

[0080]

[0081] Then, the wind power comprehensive forecast deviation of the i-th period is obtained by the wind power fluctuation deviation and wind power forecast deviation value: The photovoltaic comprehensive forecast deviation of the i-th period is obtained by the photovoltaic fluctuation deviation and photovoltaic forecast deviation value. :

[0082]

[0083]

[0084] Finally, the photovoltaic comprehensive forecast deviation and wind power comprehensive forecast deviation are combined to obtain the new energy deviation component of the i-th period :

[0085] .

[0086] S3: Calculate the net load power and the net load power standard deviation of the target power grid, obtain a deep neural network, input the system load deviation component, the tie line deviation component, the unit deviation component, the new energy deviation component, the net load power and the net load power standard deviation into the deep neural network, obtain a deviation prediction result, and calculate a loss function for the deviation prediction result;

[0087] Furthermore, the purpose of this stage is to predict through a deep neural network, obtain a deviation prediction result, and then construct a likelihood function of the deviation prediction result, and finally obtain a loss function of the deviation prediction result. Among them, step S3 further includes:

[0088] S31: Calculating the net load power and the net load power standard deviation of the target power grid according to the new energy output forecast value, the power grid load forecast value, the planned output value for this period, and the planned exchange value for this period;

[0089] S32: Obtain a deep neural network, input the system load deviation component, the tie line deviation component, the unit deviation component, the new energy deviation component, the net load power, and the net load power standard deviation into the fully connected layer and activation function layer of the deep neural network to obtain the deviation prediction result;

[0090] S33: Construct a likelihood function of the deviation prediction result, and take the logarithm of the likelihood function to obtain the loss function of the deviation prediction result.

[0091] Regarding the above steps, the specific implementation methods in this embodiment are as follows:

[0092] First, calculate the net load power of the target power grid in the i-th period and the standard deviation of net load power in the i-th period :

[0093]

[0094]

[0095] in, is the predicted value of renewable energy output in the i-th period. In this embodiment, it is the sum of the predicted value of photovoltaic output in the i-th period and the predicted value of wind power output in the i-th period. N is the number of periods participating in the prediction.

[0096] Then, the deep neural network is obtained, and the system load deviation component, tie line deviation component, unit deviation component, new energy deviation component, net load power and net load power standard deviation of the i-th period are all input into the fully connected layer and activation function layer of the deep neural network. The deep neural network is used for calculation to obtain the parameter matrix of the Gaussian distribution of the frequency regulation capacity of the target power grid as the deviation prediction result. ,in, is the frequency regulation capacity of the power grid in the i-th period, is the expected value of the grid frequency regulation capacity, is the standard deviation of the grid frequency regulation capacity, and f() represents the parameter matrix of Gaussian distribution.

[0097] Then construct the likelihood function of the deviation prediction result :

[0098]

[0099] in, represents the maximum likelihood function of the parameters in the brackets, () means taking the maximum value of the parameters in the brackets.

[0100] Then take the logarithm of both sides of the likelihood function to obtain the loss function of the deviation prediction result :

[0101]

[0102] Among them, loss() represents the loss function of the parameters in the brackets, and exp() represents the exponential function in the brackets.

[0103] S4: Iterating the loss function to obtain a target loss function, and extracting a target prediction value from the target loss function;

[0104] Furthermore, the purpose of this stage is to iterate the loss function to obtain a target loss function, thereby obtaining a target prediction value. When iterating the loss function, an optimizer is obtained, and the parameters of the deep neural network are adjusted by the optimizer. Multiple deviation prediction results are obtained by the deep neural network, and the loss function of each deviation prediction result is calculated. The loss function with the minimum loss is found among the loss functions of each deviation prediction result, and this loss function is used as the target loss function.

[0105] Regarding the above steps, the specific implementation methods in this embodiment are as follows:

[0106] First, multiple predictions are made through the deep neural network, and an optimizer is obtained at the same time. In this embodiment, the optimizer selected is the Adam optimizer. The optimizer can adjust the internal parameters of the deep neural network according to the output of the deep neural network during the deep neural network prediction process, so as to quickly output the minimum loss function. After obtaining multiple deviation prediction results, the loss function of each deviation prediction result is calculated, and the loss function with the minimum loss is used as the target loss function. Here, the minimum loss refers to the minimum value of the loss function. The target prediction value of the i-th time period can be obtained from the target loss function.

[0107] S5: Establishing evaluation parameter screening conditions, calculating the evaluation parameters of the target prediction value, screening the target prediction value using the evaluation parameters and the evaluation parameter screening conditions to obtain a prediction result, and frequency-modulating the target power grid using the prediction result.

[0108] Furthermore, the purpose of this stage is to calculate the evaluation parameters and determine whether the target prediction value meets the demand through the evaluation parameter screening conditions, take the target prediction value that meets the demand as the prediction result, and adjust the frequency of the target power grid according to the prediction result.

[0109] Wherein, when the evaluation parameter meets the evaluation parameter screening condition, the target prediction value is used as the prediction result;

[0110] When the evaluation parameter does not meet the evaluation parameter screening condition, the confidence interval of the prediction result is changed and the target prediction value is recalculated until the target prediction value meets the evaluation parameter screening condition, and the target prediction value at this time is used as the prediction result.

[0111] Regarding the above steps, the specific implementation methods in this embodiment are as follows:

[0112] First, determine the evaluation parameter screening condition. In this embodiment, the evaluation parameter screening condition is that the evaluation parameter needs to be higher than the evaluation parameter threshold determined based on experience. Then, the evaluation parameters include the first evaluation parameter and the second evaluation parameter. The target prediction value of the i-th period is calculated and applied to the target power grid. :

[0113]

[0114] in, is the average value of the grid frequency deviation in the i-th period, K is the regional frequency response coefficient of the target grid, is the regional RMS frequency control target value of the target power grid. In this embodiment, the length of each time period is 1 minute.

[0115] In addition, the target prediction value also needs to meet the needs of the target power grid in a longer period of time. Therefore, it is necessary to calculate the second evaluation parameter with a time length of multiple periods. Assuming that the length of each period is t, in this embodiment, the test time m=Ft, and the target prediction value at this time is used as the prediction result. The target prediction value is evaluated in F periods. How many periods can make the fluctuation of the target power grid within the preset range, and the second evaluation parameter after the target prediction value of the i-th period is applied to the target power grid is calculated. :

[0116]

[0117] Among them, G is the number of qualified time periods, which means how many time periods among the F time periods can make the fluctuation of the target power grid within the preset range.

[0118] When the evaluation parameters, i.e., the first evaluation parameter and the second evaluation parameter, meet the evaluation parameter screening conditions, the target predicted value is used as the prediction result. When the evaluation parameters, i.e., the first evaluation parameter and the second evaluation parameter, do not meet the evaluation parameter screening conditions, the confidence interval of the prediction result in S22 needs to be changed and the target predicted value is recalculated until the target predicted value meets the evaluation parameter screening conditions. The target predicted value at this time is used as the prediction result. The prediction result is the target power grid frequency regulation capacity for the i-th time period. The frequency regulation capacity demand is determined based on the target power grid frequency regulation capacity, and the target power grid is frequency regulated.

[0119] The method provided by the present invention realizes accurate prediction of the frequency regulation capacity of the power grid through probability density function and confidence interval, thereby improving the accuracy and efficiency of frequency regulation of the power grid.

[0120] The power grid frequency regulation device based on probability density provided by the present invention is described below. The power grid frequency regulation device based on probability density described below and the power grid frequency regulation method based on probability density described above can refer to each other.

[0121] Figure 2 The structural diagram of the power grid frequency regulation system based on probability density is shown as follows: Figure 2 As shown, the method for executing the power grid frequency regulation method based on probability density as described above includes:

[0122] Deviation component module 100: used to obtain operating data of the target power grid, and calculate the system load deviation component, tie line deviation component and unit deviation component of the target power grid based on the operating data;

[0123] New energy deviation component module 200: used to obtain a new energy output prediction model, predict the new energy power generation equipment in the target power grid using the new energy output prediction model to obtain a prediction result, construct a probability density function of the prediction result and obtain a new energy prediction deviation of the new energy power generation equipment, and calculate a new energy deviation component based on the new energy prediction deviation;

[0124] Loss function module 300: used to calculate the net load power and net load power standard deviation of the target power grid, obtain a deep neural network, input the system load deviation component, the tie line deviation component, the unit deviation component, the new energy deviation component, the net load power and the net load power standard deviation into the deep neural network, obtain a deviation prediction result, and calculate a loss function for the deviation prediction result;

[0125] Target prediction value module 400: configured to iterate the loss function to obtain a target loss function, and extract a target prediction value from the target loss function;

[0126] The power grid frequency regulation module 500 is used to establish evaluation parameter screening conditions, calculate the evaluation parameters of the target prediction value, filter the target prediction value according to the evaluation parameters and the evaluation parameter screening conditions, obtain the prediction result, and regulate the frequency of the target power grid according to the prediction result.

[0127] on the other hand, Figure 3 An example of a physical structure diagram of an electronic device is shown below. Figure 3 As shown, the electronic device may include: a processor 810, a communication interface 820, a memory 830, and a communication bus 840, wherein the processor 810, the communication interface 820, and the memory 830 communicate with each other via the communication bus 840. The processor 810 may call the logic instructions in the memory 830 to execute a power grid frequency regulation method based on probability density, which includes:

[0128] S1: Acquire operating data of a target power grid, and calculate a system load deviation component, a tie line deviation component, and a unit deviation component of the target power grid based on the operating data;

[0129] S2: Obtain a new energy output prediction model, predict the new energy power generation equipment in the target power grid using the new energy output prediction model to obtain a prediction result, construct a probability density function of the prediction result and obtain a new energy prediction deviation of the new energy power generation equipment, and calculate a new energy deviation component using the new energy prediction deviation;

[0130] S3: Calculate the net load power and the net load power standard deviation of the target power grid, obtain a deep neural network, input the system load deviation component, the tie line deviation component, the unit deviation component, the new energy deviation component, the net load power and the net load power standard deviation into the deep neural network, obtain a deviation prediction result, and calculate a loss function for the deviation prediction result;

[0131] S4: Iterating the loss function to obtain a target loss function, and extracting a target prediction value from the target loss function;

[0132] S5: Establishing evaluation parameter screening conditions, calculating the evaluation parameters of the target prediction value, screening the target prediction value using the evaluation parameters and the evaluation parameter screening conditions to obtain a prediction result, and frequency-modulating the target power grid using the prediction result.

[0133] Furthermore, the logic instructions in the aforementioned memory 830 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product, stored in a storage medium, includes instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0134] On the other hand, the present invention further provides a computer program product, comprising a computer program stored on a non-transitory computer-readable storage medium, wherein the computer program comprises program instructions. When the program instructions are executed by a computer, the computer is capable of performing the probability density-based power grid frequency regulation method provided by the above methods, the method comprising:

[0135] S1: Acquire operating data of a target power grid, and calculate a system load deviation component, a tie line deviation component, and a unit deviation component of the target power grid based on the operating data;

[0136] S2: Obtain a new energy output prediction model, predict the new energy power generation equipment in the target power grid using the new energy output prediction model to obtain a prediction result, construct a probability density function of the prediction result and obtain a new energy prediction deviation of the new energy power generation equipment, and calculate a new energy deviation component using the new energy prediction deviation;

[0137] S3: Calculate the net load power and the net load power standard deviation of the target power grid, obtain a deep neural network, input the system load deviation component, the tie line deviation component, the unit deviation component, the new energy deviation component, the net load power and the net load power standard deviation into the deep neural network, obtain a deviation prediction result, and calculate a loss function for the deviation prediction result;

[0138] S4: Iterating the loss function to obtain a target loss function, and extracting a target prediction value from the target loss function;

[0139] S5: Establishing evaluation parameter screening conditions, calculating the evaluation parameters of the target prediction value, screening the target prediction value using the evaluation parameters and the evaluation parameter screening conditions to obtain a prediction result, and frequency-modulating the target power grid using the prediction result.

[0140] In another aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform the probability density-based power grid frequency regulation method provided by the above methods, the method comprising:

[0141] S1: Acquire operating data of a target power grid, and calculate a system load deviation component, a tie line deviation component, and a unit deviation component of the target power grid based on the operating data;

[0142] S2: Obtain a new energy output prediction model, predict the new energy power generation equipment in the target power grid using the new energy output prediction model to obtain a prediction result, construct a probability density function of the prediction result and obtain a new energy prediction deviation of the new energy power generation equipment, and calculate a new energy deviation component using the new energy prediction deviation;

[0143] S3: Calculate the net load power and the net load power standard deviation of the target power grid, obtain a deep neural network, input the system load deviation component, the tie line deviation component, the unit deviation component, the new energy deviation component, the net load power and the net load power standard deviation into the deep neural network, obtain a deviation prediction result, and calculate a loss function for the deviation prediction result;

[0144] S4: Iterating the loss function to obtain a target loss function, and extracting a target prediction value from the target loss function;

[0145] S5: Establishing evaluation parameter screening conditions, calculating the evaluation parameters of the target prediction value, screening the target prediction value using the evaluation parameters and the evaluation parameter screening conditions to obtain a prediction result, and frequency-modulating the target power grid using the prediction result.

[0146] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0147] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0148] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A power grid frequency modulation method based on probability density, characterized in that: include: S1: Acquire operating data of a target power grid, and calculate a system load deviation component, a tie line deviation component, and a unit deviation component of the target power grid based on the operating data; S2: Obtain a new energy output prediction model, predict the new energy power generation equipment in the target power grid using the new energy output prediction model to obtain a prediction result, construct a probability density function of the prediction result and obtain a new energy prediction deviation of the new energy power generation equipment, and calculate a new energy deviation component using the new energy prediction deviation; S3: Calculate the net load power and the net load power standard deviation of the target power grid, obtain a deep neural network, input the system load deviation component, the tie line deviation component, the unit deviation component, the new energy deviation component, the net load power, and the net load power standard deviation into the deep neural network to obtain a deviation prediction result, and calculate a loss function for the deviation prediction result; wherein step S3 further includes: S31: Calculating the net load power and the net load power standard deviation of the target power grid according to the new energy output forecast value, the power grid load forecast value, the planned output value for this period, and the planned exchange value for this period; S32: Obtain a deep neural network, input the system load deviation component, the tie line deviation component, the unit deviation component, the new energy deviation component, the net load power, and the net load power standard deviation into the fully connected layer and activation function layer of the deep neural network to obtain the deviation prediction result; S33: constructing a likelihood function of the deviation prediction result, and taking the logarithm of the likelihood function to obtain the loss function of the deviation prediction result; S4: Iterating the loss function to obtain a target loss function, and extracting a target prediction value from the target loss function; S5: Establishing evaluation parameter screening conditions, calculating the evaluation parameters of the target prediction value, screening the target prediction value using the evaluation parameters and the evaluation parameter screening conditions to obtain a prediction result, and frequency-modulating the target power grid using the prediction result.

2. The method for power grid frequency modulation based on probability density according to claim 1, characterized in that: Step S1 further comprises: S11: determining the target power grid, obtaining the operating data of the target power grid, calculating the system load forecast deviation and the system load fluctuation deviation of the target power grid based on the operating data, and calculating the system load deviation component based on the system load forecast deviation and the system load fluctuation deviation; S12: acquiring a planned exchange value after a period of time and a planned exchange value in the current period of time of the tie line from the operation data, and obtaining the tie line deviation component by using the planned exchange value after a period of time and the planned exchange value in the current period of time; S13: Obtaining a planned output value after a period of time and a planned output value for the current period of time of the generator set from the operation data, and calculating the generator set deviation component by using the planned output value after a period of time and the planned output value for the current period of time.

3. The method for power grid frequency modulation based on probability density according to claim 1, characterized in that: Step S2 further comprises: S21: Determine the prediction time and the new energy power generation equipment in the target power grid, obtain a new energy output prediction model, and perform multiple predictions on the power generation of the new energy power generation equipment according to the prediction time to obtain a prediction result. S22: determining a confidence interval of the prediction result, constructing a probability density function of the prediction result according to the probability distribution of the prediction result, and obtaining the new energy prediction deviation according to the confidence interval and the probability density function; S23: Obtaining a new energy output prediction value of the new energy power generation equipment through a probability density function of the prediction result, and obtaining the new energy deviation component through the new energy prediction deviation and the new energy output prediction value.

4. The method for power grid frequency modulation based on probability density according to claim 1, characterized in that: In step S4, when iterating the loss function, an optimizer is obtained, and the parameters of the deep neural network are adjusted by the optimizer. A plurality of deviation prediction results are obtained by the deep neural network, and the loss function of each deviation prediction result is calculated. The loss function with the minimum loss is found in the loss function of each deviation prediction result, and is used as the target loss function.

5. The method for power grid frequency modulation based on probability density according to claim 1, characterized in that: In step S5, when the evaluation parameter meets the evaluation parameter screening condition, the target prediction value is used as the prediction result; When the evaluation parameter does not meet the evaluation parameter screening condition, the confidence interval of the prediction result is changed and the target prediction value is recalculated until the target prediction value meets the evaluation parameter screening condition, and the target prediction value at this time is used as the prediction result.

6. A power grid frequency regulation system based on probability density, configured to execute the power grid frequency regulation method based on probability density according to any one of claims 1 to 5, characterized in that: include: Deviation component module: used to obtain the operating data of the target power grid, and calculate the system load deviation component, tie line deviation component and unit deviation component of the target power grid based on the operating data; New energy deviation component module: used to obtain a new energy output prediction model, predict the new energy power generation equipment in the target power grid using the new energy output prediction model to obtain a prediction result, construct a probability density function of the prediction result and obtain the new energy prediction deviation of the new energy power generation equipment, and calculate the new energy deviation component based on the new energy prediction deviation; Loss function module: used to calculate the net load power and net load power standard deviation of the target power grid, obtain a deep neural network, input the system load deviation component, the tie line deviation component, the unit deviation component, the new energy deviation component, the net load power and the net load power standard deviation into the deep neural network, obtain a deviation prediction result, and calculate a loss function for the deviation prediction result; Target prediction value module: used for iterating the loss function to obtain a target loss function, and extracting a target prediction value from the target loss function; The power grid frequency modulation module is used to establish evaluation parameter screening conditions, calculate the evaluation parameters of the target prediction value, filter the target prediction value according to the evaluation parameters and the evaluation parameter screening conditions, obtain the prediction result, and modulate the frequency of the target power grid according to the prediction result.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the power grid frequency regulation method based on probability density are implemented as described in any one of claims 1 to 5.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the power grid frequency regulation method based on probability density are implemented as described in any one of claims 1 to 5.

9. A computer program product, comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, characterized in that: When the program instructions are executed by a computer, the computer can perform the steps of the power grid frequency regulation method based on probability density as claimed in any one of claims 1 to 5.

Images