A method and device for weekly-daily power distribution considering uncertainty of new energy sources

Abstract

The present invention discloses a weekly-daily electricity distribution method and device considering the uncertainty of new energy sources, the method comprising: inputting new energy electricity data to obtain a new energy data curve; decomposing the new energy data curve by the EEMD method, and screening the decomposition results by the SE algorithm to obtain multiple intrinsic mode function components; comparing each of the intrinsic mode function components with a preset threshold value to obtain a weekly-daily electricity distribution curve of an electricity trend component and / or a weekly-daily electricity distribution curve of electricity details; merging the weekly-daily electricity distribution curve of the electricity trend component and the weekly-daily electricity distribution curve of electricity details by the weighted moving average method; and outputting the merged weekly-daily new energy electricity distribution curve. The method is better adapted to the volatility of new energy electricity, can effectively improve the solution efficiency and convergence, reduce the impact of production capacity fluctuations on the operation of the power system, and ensure the safe operation of the power system.

Description

Technical Field

[0001] The present invention relates to the field of new energy technologies, and in particular to a method and device for weekly-daily electricity distribution taking into account the uncertainty of new energy. Background Art

[0002] In recent years, the development and utilization of renewable energy has become an inevitable trend in my country's energy transformation. However, due to the volatility, randomness and uncertainty of new energy, there is a difference between the actual power generation and the predicted power generation, which brings major challenges to the large-scale consumption of new energy and the safe operation of the power system. Due to the large restrictions on the environmental conditions for new energy power generation, its production capacity fluctuations may have a significant impact on the operation of the power system.

[0003] There are currently a variety of research methods to reduce the impact of uncertainty in renewable energy power generation. The first is to increase the spare capacity of renewable energy installed capacity. As a traditional method to reduce the impact of renewable energy uncertainty, this method can guarantee the reliability and stability of system operation to a large extent, but it will significantly increase the cost of power generation. The second is new energy optimization modeling based on the scenario method. This method describes its random characteristics by constructing a variety of possible renewable energy power generation scenarios. While ensuring that the constraints of the system and the generator set are met, it effectively responds to the random volatility of the renewable energy units through the reasonable scheduling of non-renewable energy generator sets, thereby minimizing the total operating cost of the system. The essence of this method is to convert the uncertainty optimization problem into a deterministic optimization problem for solution by sampling the error scenarios of renewable energy random variables. However, this method has high requirements on the accuracy and quality of the data, and the error scenarios generated according to the specific probability distribution function may not be able to fully simulate the actual situation. Summary of the invention

[0004] The purpose of the present invention is to overcome the shortcomings of the above-mentioned prior art and provide a weekly-daily electricity allocation method taking into account the uncertainty of new energy sources, so as to better adapt to the volatility of new energy electricity, effectively improve the solution efficiency and convergence, reduce the impact of production capacity fluctuations on power system operation, and ensure the safe operation of the power system.

[0005] To achieve the above object, the technical solution of the present invention is:

[0006] In a first aspect, the present invention provides a weekly-daily electricity allocation method considering the uncertainty of new energy sources, comprising:

[0007] Input new energy power data to obtain new energy data curve;

[0008] The new energy data curve is decomposed by using the EEMD method, and the decomposition result is screened by using the SE algorithm to obtain multiple intrinsic mode function components;

[0009] Compare each of the intrinsic mode function components with a preset threshold value. If it is less than the threshold value, the new energy power trend component is input into a pre-built weekly-daily robust optimization power distribution model considering the uncertainty of new energy, so as to obtain a weekly-daily power distribution curve of the power trend component; if it is greater than the threshold value, the new energy power detail component is input into a pre-built weekly-daily robust optimization power distribution model considering the uncertainty of new energy, so as to obtain a weekly-daily power distribution curve of the power detail;

[0010] The power trend component weekly-daily power distribution curve and the power detail weekly-daily power distribution curve are combined by using a weighted moving average method;

[0011] Output the combined weekly-daily new energy power distribution curve.

[0012] In a second aspect, the present invention provides an electronic device, comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus;

[0013] Memory, used to store computer programs;

[0014] The processor is used to implement the steps of the weekly-daily electricity allocation method considering the uncertainty of new energy as described above when executing the program stored in the memory.

[0015] Compared with the prior art, the present invention has the following beneficial effects:

[0016] The present invention provides a weekly-daily electricity allocation method that takes into account the uncertainty of new energy sources, which can better adapt to the volatility of new energy electricity, effectively improve the solution efficiency and convergence, reduce the impact of production capacity fluctuations on power system operation, and ensure the safe operation of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A flow chart of a weekly-daily electricity allocation method considering the uncertainty of new energy sources provided by an embodiment of the present invention;

[0018] Figure 2 A schematic diagram of the composition of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In this application, the relevant abbreviations and key terms are defined as follows:

[0020] EMD: Empirical Mode Decomposition

[0021] EEMD: Ensemble Empirical Mode Decomposition, ensemble empirical mode decomposition

[0022] WMA: Weighted Moving Average,

[0023] IMFS: Intrinsic Mode Function, intrinsic mode function

[0024] SE:Sample Entropy, sample entropy

[0025] SMA: Simple Moving Average, simple moving average

[0026] Example:

[0027] The technical solution of the present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0028] Due to the large restrictions on the environmental conditions of renewable energy power generation, its capacity fluctuation may have a significant impact on the operation of the power system. In order to obtain better new energy data preprocessing effects, reduce the impact of capacity fluctuations on the operation of the power system, ensure that the system fully copes with various uncertainties and fluctuations in renewable energy power generation, and ensure the safe operation of the power system, refer to Figure 1 As shown, this embodiment provides a weekly-daily electricity allocation method considering the uncertainty of new energy sources, including the following steps:

[0029] Input new energy power data to obtain new energy data curve;

[0030] Specifically, in this step, the input new energy power data includes the new energy planned power generation (E w,z,d , E v,z,d ), the upper and lower limits of renewable energy generation capacity The upper and lower bounds of predicted power generation from new energy sources

[0031] The new energy data curve is decomposed by using the EEMD method, and the decomposition result is screened by using the SE algorithm to obtain multiple intrinsic mode function components.

[0032] In this step, the new energy data curve is decomposed by EEMD, and the decomposition results are screened by SE algorithm, which can effectively reduce the volatility of the model input data. Compared with the traditional data decomposition processing method, this method fully considers the correlation between data, does not increase the complexity of data processing results, can better adapt to the volatility of new energy electricity, and can effectively improve the solution efficiency and convergence of the model.

[0033] Each of the intrinsic mode function components is compared with a preset threshold value (in this embodiment, the preset threshold value is 0.7). If it is less than the threshold value, the new energy electricity trend component is input into a pre-built weekly-daily robust optimization electricity distribution model considering the uncertainty of new energy, so as to obtain the weekly-daily electricity distribution curve of the electricity trend component; if it is greater than the threshold value, the new energy electricity detail component is input into a pre-built weekly-daily robust optimization electricity distribution model considering the uncertainty of new energy, so as to obtain the weekly-daily electricity distribution curve of the electricity detail.

[0034] In this step, based on the weekly-daily robust optimization power distribution model considering the uncertainty of new energy, the uncertainty problem is converted into a deterministic problem through uncertainty parameter transformation, ensuring that the optimal solution shows good stability under various uncertain conditions, reducing the impact of production capacity fluctuations on power system operation, and ensuring the safe operation of the power system.

[0035] The power trend component weekly-daily power distribution curve and the power detail weekly-daily power distribution curve are combined using a weighted moving average method.

[0036] In this step, the weighted moving average method is used to process the model results, assigning different weights to multiple groups of time series data, and merging multiple groups of data curves. The weighted moving average method merges the model results so that the model solution results can be flexibly adjusted according to the actual application situation. Compared with the traditional simple moving average method (the commonly used data smoothing and analysis method is the simple moving average method (Simple Moving Average, SMA). In this method, each data point is assigned the same weight, which cannot reflect the weight relationship between each corresponding data point), the merged result is more accurate and can be closer to the actual application requirements.

[0037] Output the combined weekly-daily new energy power distribution curve.

[0038] It can be seen that the weekly-daily electricity allocation method that takes into account the uncertainty of new energy provided in this embodiment can better adapt to the volatility of new energy electricity, can effectively improve the solution efficiency and convergence, reduce the impact of production capacity fluctuations on power system operation, and ensure the safe operation of the power system.

[0039] Commonly used methods for processing new energy data include empirical mode decomposition (EMD), integrated empirical mode decomposition (EEMD) and wavelet decomposition. EMD is an adaptive method for nonlinear and non-stationary signals. It can automatically determine the frequency and number of decompositions according to the characteristics of the data itself, but when processing signals with similar scales or frequencies, mode aliasing may occur; EEMD solves the mode aliasing problem of EMD to a certain extent by adding white noise to the original data. However, in the traditional EEMD method, the correlation between the components is ignored, which may increase the complexity of the data; wavelet decomposition can locally decompose the new energy data in both the time domain and the frequency domain, but the accuracy of the decomposition result depends on the selection of wavelet basis functions. To this end, this embodiment uses the EEMD method to decompose the new energy data curve, and uses the SE algorithm to screen the decomposition results to obtain multiple intrinsic mode function components.

[0040] Specifically, the EEMD method includes:

[0041] 1201. Set the amplitude of white noise and the maximum number of iterations of EMD.

[0042] In EEMD, there are two important parameters, one is the amplitude k of white noise, and the other is the maximum number of iterations M of EMD. In order to maintain generality, M is taken as 100 and the value range of k is 0.05-0.5.

[0043] 1202. Add a white noise sequence to the signal;

[0044] 1203. EMD decomposes the signal with white noise added;

[0045] 1204. Repeat steps 1202 and 1203 for a certain number of white noises each time to obtain corresponding IMF components; calculate the average value of all corresponding IMFs as the final result of each IMF, then calculate the average value of each residual component, and take the average value as the final result of the residual.

[0046]

[0047] Represents the components of the IMF, Represents the components of RES.

[0048] To consider the correlation between the components. This embodiment uses the sample entropy theory to reorganize these components with related characteristics. For a given k, r, N, where k represents the embedding dimension, r represents the tolerance, and N represents the number of data points. SampEn(N,k,r) is the negative logarithm of the conditional probability. For the data sequence {x i}={x(1),...,x(N)}, the specific algorithm of sample entropy (SE) is:

[0049] 1211. Construct a sequence {x i} constitutes an m-dimensional vector

[0050] X(i)=[x(i),x(i+1),…,x(i+k-1)] (2)

[0051] 1212. Define the distance d between vectors X(i) and X(j) k (X(i),X(j)) as the absolute maximum difference between their scalar components

[0052]

[0053] 1213. For a given value of r, calculate d k The number of (X(i),X(j))≤r, and then calculate the ratio of NK, which is defined as:

[0054]

[0055] Where r is the threshold value, as a noise filter, r>0; i=1,…,N-k+1.

[0056] 1214, The mean of is expressed as:

[0057]

[0058] 1215. Increase the number of iterations to k+1 and repeat steps 1214 to 1215. The average value is expressed as:

[0059]

[0060] 1216. For a finite data length N, SampEn is estimated to be

[0061] SampEn(N,k,r)=-ln[B k+1 (r) / B k (r)] (7)

[0062] Where k represents the embedding dimension, r represents the tolerance, and N represents the number of data points; SampEn(N,k,r) is the negative logarithm of the conditional probability, {x i}={x(1),...,x(N)} is the data sequence.

[0063] In a specific embodiment, the objective function of the weekly-daily robust optimization power distribution model considering the uncertainty of new energy sources is:

[0064]

[0065] In the formula, F ewd is the total electricity market revenue of renewable energy on the dth day in the zth week, C DC is the daily new energy operating cost on the dth day in the zth week, is the actual wind power generation on the dth day in the zth week, P w is the wind power price on the dth day in the zth week, is the actual photovoltaic power generation on the dth day in the zth week, P v is the photovoltaic electricity price on the dth day in the zth week, W is the total number of weeks in a year, and D is the total number of days in a week.

[0066] The model aims to allocate the electricity volume traded in the power market of renewable energy on a weekly time scale to a daily time scale according to the goal of maximizing total revenue, and then use the robust optimization method to transform the uncertainty optimization problem into a deterministic optimization problem for solution. The weekly-daily robust optimization electricity allocation model considering the uncertainty of renewable energy can ensure the robustness of the optimization results.

[0067] The constraints of the weekly-daily robust optimization power distribution model considering the uncertainty of new energy sources include:

[0068] Photovoltaic power generation constraints:

[0069]

[0070] In the formula, E v,z,d is the planned power generation of photovoltaic power generation on the dth day in the zth week, is the actual power generation of photovoltaic power generation on the dth day in the zth week, is the lower limit of the photovoltaic power generation allowed on the dth day in the zth week, is the upper bound of the predicted power generation on the dth day in the zth week of photovoltaic power generation, is the lower limit of the photovoltaic power generation allowed on the dth day in the zth week, is the lower bound of the predicted power generation on the dth day in the zth week of photovoltaic power generation, is the upper limit of the photovoltaic power generation allowed on the dth day in the zth week, is the power generation under the maximum power supply capacity of photovoltaic power generation on the dth day in the zth week, It is the power generation under the minimum power supply capacity of photovoltaic power plant on the dth day in the zth week.

[0071] Wind power generation constraints:

[0072]

[0073] In the formula, E w,z,d is the planned power generation of wind power on the dth day in the zth week, is the actual power generation of wind power on the dth day in the zth week, is the lower limit of the wind power generation allowed on the dth day in the zth week, is the upper bound of wind power generation forecast for the dth day in the zth week, is the lower limit of the wind power generation allowed on the dth day in the zth week, is the lower bound of wind power generation forecast for the dth day in the zth week, is the upper limit of wind power generation allowed on the dth day in the zth week, is the power generation of wind power at the maximum power supply capacity on the dth day in the zth week, It is the power generation under the minimum power supply capacity of wind power on the dth day in the zth week.

[0074] Constraints on total transaction volume of new energy:

[0075]

[0076] In the formula, E y is the total annual power generation of renewable energy, is the actual power generation of wind power on the dth day in the zth week, is the actual power generation of photovoltaic power on the dth day in the zth week;

[0077] Constraints on weekly electricity consumption deviation assessment for new energy sources:

[0078]

[0079] In the formula, is the actual power generation of wind power on the dth day in the zth week, is the actual power generation of photovoltaic power generation on the dth day in the zth week, is the actual renewable energy power generation in week z;

[0080] Photovoltaic ramp rate constraints:

[0081]

[0082] They are the actual photovoltaic output at the two moments before and after. They are the allowable down-ramp rate and up-ramp rate of photovoltaic respectively;

[0083] Wind power ramp rate constraints:

[0084]

[0085] are the actual wind power output at the two moments before and after, P v,t , P v,t-1 They are the planned photovoltaic output at the two moments before and after. They are the allowable down-ramp rate and up-ramp rate for wind power respectively;

[0086] Network security constraints:

[0087]

[0088] In the formula, is the line distribution factor of wind power and photovoltaic power, is the actual power generation of wind power on the dth day in the zth week, is the actual power generation of photovoltaic power generation on the dth day in the zth week, It is the maximum power limit of the key section on the dth day in the zth week.

[0089] The uncertainties in the constraints may make the model difficult to solve. Therefore, equations (12) and (21) can be transformed into forms containing certainties through mathematical transformation, so that the model can be converted into a linear programming model that is easy to solve. In this embodiment, a robust optimization method is used to process the constraints in the model:

[0090]

[0091] It is equivalent to solving the following problem:

[0092]

[0093] Among them, m i is a constant, an uncertain quantity The constraints can be replaced by the upper and lower bounds of their values, and transformed into constraints on the upper and lower bounds of the uncertain quantity;

[0094] By using the above method to transform equation (12) and equation (21), we can obtain:

[0095]

[0096] In order to obtain the final power distribution, the solution results of the trend component and the detail component need to be combined using the weighted moving average method (WMA). The basic principle is as follows:

[0097] Suppose that n curve data points with the same data length are The weights are w1, w2...w n And satisfy w1+w2.+...+w n-1 +w n =1.

[0098] Calculate their weighted moving averages respectively

[0099]

[0100] Will n The weighted moving average result of the curves is calculated according to the given weight w k Merge to get the final merged curve:

[0101]

[0102] like Figure 2 As shown, the embodiment of the present application also provides an electronic device, including a processor 111, a communication interface 112, a memory 113 and a communication bus 114, wherein the processor 111, the communication interface 112, and the memory 113 communicate with each other through the communication bus 114; the memory 113 is used to store computer programs; the processor 111 is used to implement the steps of the weekly-daily power distribution method considering the uncertainty of new energy provided by any of the above method embodiments when executing the program stored in the memory 113. .

[0103] An embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the weekly-daily electricity allocation method considering the uncertainty of new energy are implemented as provided in any of the aforementioned method embodiments.

[0104] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device that includes a series of elements includes not only those elements, but also other elements that are not explicitly listed, or also includes elements that are inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprises a..." does not exclude the existence of other identical elements in the process, method, article or device that includes the elements.

[0105] The above embodiments are only for illustrating the technical concept and features of the present invention, and their purpose is to enable ordinary technicians in the field to understand the content of the present invention and implement it accordingly, and they cannot be used to limit the protection scope of the present invention. Any equivalent changes or modifications made based on the essence of the content of the present invention should be included in the protection scope of the present invention.

Claims

1. A weekly-daily electricity allocation method considering the uncertainty of new energy, characterized in that: include: Input new energy power data to obtain new energy data curve; The new energy data curve is decomposed by using the EEMD method, and the decomposition result is screened by using the SE algorithm to obtain multiple intrinsic mode function components; Compare each of the intrinsic mode function components with a preset threshold value. If it is less than the threshold value, the new energy power trend component is input into a pre-built weekly-daily robust optimization power distribution model considering the uncertainty of new energy, so as to obtain a weekly-daily power distribution curve of the power trend component; if it is greater than the threshold value, the new energy power detail component is input into a pre-built weekly-daily robust optimization power distribution model considering the uncertainty of new energy, so as to obtain a weekly-daily power distribution curve of the power detail; The power trend component weekly-daily power distribution curve and the power detail weekly-daily power distribution curve are combined by using a weighted moving average method; Output the combined weekly-daily new energy power distribution curve.

2. The weekly-daily electricity allocation method considering the uncertainty of new energy as claimed in claim 1, characterized in that: The EEMD method includes: 1201. Set the amplitude of white noise and the maximum number of iterations of EMD 1202. Add a white noise sequence to the signal; 1203. EMD decomposes the signal with white noise added; 1204. Repeat steps 1202 and 1203 for a certain number of white noises each time to obtain corresponding IMF components; calculate the average value of all corresponding IMFs as the final result of each IMF, then calculate the average value of each residual component, and take the average value as the final result of the residual. Represents the components of the IMF, Represents the components of RES.

3. The weekly-daily electricity allocation method considering the uncertainty of new energy as claimed in claim 2, characterized in that: The SE algorithm includes: 1211. Construct a sequence {x i } constitutes an m-dimensional vector X(i)=[x(i),x(i+1),…,x(i+k-1)] (2) 1212. Define the distance d between vectors X(i) and X(j) k (X(i),X(j)) as the absolute maximum difference between their scalar components; 1213. For a given value of r, calculate d k The number of (X(i),X(j))≤r, and then calculate the ratio of NK, which is defined as: Where r is the threshold value, as a noise filter, r>0; i=1,…,N-k+1. 1214, The mean of is expressed as: 1215. Increase the number of iterations to k+1 and repeat steps 1214 to 1215. The average value is expressed as: 1216. For a finite data length N, SampEn is estimated to be SampEn(N,k,r)_-ln[B k+1 (r) / B k (r)] (7) Where k represents the embedding dimension, r represents the tolerance, and N represents the number of data points; SampEn(N,k,r) is the negative logarithm of the conditional probability, {x i }={x(1),...,x(N)} is the data sequence.

4. The weekly-daily electricity allocation method considering the uncertainty of new energy as claimed in claim 1, characterized in that: The objective function of the weekly-daily robust optimization power distribution model considering the uncertainty of new energy is: In the formula, F ewd is the total electricity market revenue of renewable energy on the dth day in the zth week, C DC is the daily new energy operating cost on the dth day in the zth week, is the actual wind power generation on the dth day in the zth week, P w is the wind power price on the dth day in the zth week, is the actual photovoltaic power generation on the dth day in the zth week, P v is the photovoltaic electricity price on the dth day in the zth week, W is the total number of weeks in a year, and D is the total number of days in a week.

5. The weekly-daily electricity allocation method considering the uncertainty of new energy sources as claimed in claim 4, characterized in that: The constraints of the weekly-daily robust optimization power distribution model considering the uncertainty of new energy sources include: Photovoltaic power generation constraints: In the formula, E v,z,d is the planned power generation of photovoltaic power generation on the dth day in the zth week, is the actual power generation of photovoltaic power generation on the dth day in the zth week, is the lower limit of the photovoltaic power generation allowed on the dth day in the zth week, is the upper bound of the predicted power generation on the dth day in the zth week of photovoltaic power generation, is the lower limit of the photovoltaic power generation allowed on the dth day in the zth week, is the lower bound of the predicted power generation on the dth day in the zth week of photovoltaic power generation, is the upper limit of the photovoltaic power generation allowed on the dth day in the zth week, is the power generation under the maximum power supply capacity of photovoltaic power generation on the dth day in the zth week, It is the power generation under the minimum power supply capacity of photovoltaic power plant on the dth day in the zth week.

6. The weekly-daily electricity allocation method considering the uncertainty of new energy sources as claimed in claim 5, characterized in that: The constraints of the weekly-daily robust optimization power distribution model considering the uncertainty of new energy sources also include: wind power generation constraints: In the formula, E w,z,d is the planned power generation of wind power on the dth day in the zth week, is the actual power generation of wind power on the dth day in the zth week, is the lower limit of the wind power generation allowed on the dth day in the zth week, is the upper bound of wind power generation forecast for the dth day in the zth week, is the lower limit of the wind power generation allowed on the dth day in the zth week, is the lower bound of wind power generation forecast for the dth day in the zth week, is the upper limit of wind power generation allowed on the dth day in the zth week, is the power generation of wind power at the maximum power supply capacity on the dth day in the zth week, It is the power generation under the minimum power supply capacity of wind power on the dth day in the zth week.

7. The weekly-daily electricity allocation method considering the uncertainty of new energy sources as claimed in claim 6, characterized in that: The constraints of the weekly-daily robust optimization power distribution model considering the uncertainty of new energy sources also include: Constraints on total transaction volume of new energy: In the formula, E y is the total annual power generation of renewable energy, is the actual power generation of wind power on the dth day in the zth week, is the actual power generation of photovoltaic power on the dth day in the zth week; Constraints on weekly electricity consumption deviation assessment for new energy sources: In the formula, is the actual power generation of wind power on the dth day in the zth week, is the actual power generation of photovoltaic power generation on the dth day in the zth week, is the actual renewable energy power generation in week z; Photovoltaic ramp rate constraints: They are the actual photovoltaic output at the two moments before and after. They are the allowable down-ramp rate and up-ramp rate of photovoltaic respectively; Wind power ramp rate constraints: are the actual wind power output at the two moments before and after, P v,t , P v,t-1 They are the planned photovoltaic output at the two moments before and after. They are the allowable down-ramp rate and up-ramp rate for wind power respectively; Network security constraints: In the formula, is the line distribution factor of wind power and photovoltaic power, is the actual power generation of wind power on the dth day in the zth week, is the actual power generation of photovoltaic power generation on the dth day in the zth week, It is the maximum power limit of the critical section on the dth day in the zth week.

8. The weekly-daily electricity allocation method considering the uncertainty of new energy sources as claimed in claim 7, characterized in that: The robust optimization method is used to deal with the constraints of the weekly-daily robust optimization power distribution model considering the uncertainty of new energy: This is equivalent to solving the following problem: Among them, m i is a constant, an uncertain quantity The constraints can be replaced by the upper and lower bounds of their values, and transformed into constraints on the upper and lower bounds of the uncertain quantity; By using the above method to transform equation (12) and equation (21), we can obtain:

9. The weekly-daily electricity allocation method considering the uncertainty of new energy sources as claimed in claim 1, characterized in that: The method of combining the weekly-daily power distribution curve of the power trend component and the weekly-daily power distribution curve of the power detail by using the weighted moving average method includes: Suppose that n curve data points with the same data length are The weights are w1, w2...w n And satisfy w1+w2.+...+w n-1 +w n =1. Calculate their weighted moving averages respectively The weighted moving average result of n curves is calculated according to the given weight w. k Merge to get the final merged curve y t :

10. An electronic device, characterized in that: It includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus; Memory, used to store computer programs; The processor is used to implement the steps of the weekly-daily electricity distribution method considering the uncertainty of new energy sources as described in any one of claims 1-9 when executing the program stored in the memory.

Images