Source load balance degree quantitative evaluation method and device based on Renyi entropy theory and medium

Abstract

The invention relates to a Renyi entropy theory-based source load balance degree quantitative evaluation method and device, and a medium, and the method comprises the following steps: obtaining a daily load curve and a distributed power supply power output curve of a power distribution network, and carrying out the preprocessing, and obtaining a net load time series data set; dividing the net load time sequence data set into a plurality of net load subsequences according to positive and negative conditions of net load values at all moments; calculating the Renyi entropy of each net load sub-sequence by adopting a Renyi entropy theory; and calculating a source load balance degree based on the Renyi entropy of each net load sub-sequence, and completing a quantitative evaluation process of the source load balance degree. Compared with the prior art, the method has the advantages of accurately quantifying the source load balance degree and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of quantifying and evaluating the matching degree of power sources and loads, and particularly to a method, device and medium for quantifying and evaluating the balance degree of power sources and loads based on the Renyi entropy theory. Background Art

[0002] With the proposal of the "dual carbon" goal and the construction of a new power system, the penetration rates of distributed power sources (DG), flexible loads, energy storage and other resources represented by wind power and photovoltaic power have been increasing continuously. The operation of power sources and loads in the power system shows significant characteristics of randomness, diversity and strong uncertainty. These changes not only increase the complexity of the power system, but also pose great challenges to the real-time balance of the power distribution network power.

[0003] On the one hand, the output of distributed power sources such as wind power and photovoltaic power is affected by natural conditions and has significant intermittency and uncertainty, resulting in a substantial increase in the system regulation demand. At the same time, the uncertainty on the load side is also increasing. For example, the proportion of the tertiary industry and residential electricity consumption has risen, making the peak load characteristics more prominent and the system peak-valley difference further increased. In addition, with the increase in the installed capacity of photovoltaic power, the net load shows the characteristics of a "duck curve" and double peak phenomena in the morning and evening, increasing the peak shaving pressure of the system. On the other hand, the regulation ability of the power system faces bottlenecks. At present, traditional power sources such as coal-fired power, gas-fired power and pumped storage are mainly relied on to provide regulation capacity, but the regulation speed and flexibility of these resources are difficult to meet the requirements of the new power system. For example, the ramp rate of coal-fired power units is relatively slow and it is difficult to cope with the rapid fluctuations of new energy power generation.

[0004] Patent CN109256799B discloses a method for optimizing the scheduling of a new energy power system based on sample entropy. In this method, sample entropy is used to evaluate the complexity of the net load subsequence. However, sample entropy needs to calculate the similarity of all subsequences in the sequence, with a high time complexity and it is difficult to meet the real-time scheduling requirements. Secondly, the robustness of sample entropy to noise and non-stationary signals is poor, which may lead to distortion in the evaluation of the net load complexity.

[0005] Under this background, accurately characterizing the uncertainty of power sources and loads is of great significance for the research of power and electricity balance methods. Therefore, in the process of building a future new power system, it is necessary to accurately evaluate influencing factors such as flexible loads and distributed new energy. Summary of the Invention

[0006] The purpose of the present invention is to provide a method, device and medium for quantifying and evaluating the balance degree of power sources and loads based on the Renyi entropy theory, which can accurately characterize the matching degree of power source and load data in time series.

[0007] The purpose of the present invention can be achieved by the following technical solutions:

[0008] A source-load balance quantization evaluation method based on Renyi entropy theory, comprising the following steps:

[0009] Obtain the daily load curve and distributed power source power output curve of the distribution network, and perform preprocessing to obtain a net load time series dataset;

[0010] According to the positive and negative conditions of the net load values at each moment, divide the net load time series dataset into multiple net load subsequences;

[0011] Calculate the Renyi entropy of each net load subsequence using Renyi entropy theory;

[0012] Based on the Renyi entropy of each net load subsequence, calculate the source-load balance degree, and complete the quantization evaluation process of the source-load balance degree.

[0013] Furthermore, use discrete sampling technology for the preprocessing, and the steps of the preprocessing include:

[0014] Divide the daily load curve and distributed power source power output curve in hours;

[0015] According to the division result, calculate the average load parameter and distributed power source average power output value of each time period to obtain the daily load time series and distributed power source power output time series, where the daily load time series is expressed as:

[0016] {P load,t} = {p load,1 , p load,2 ,..., p load,n}

[0017] In the formula, P load,t is the daily load time series, p load,n is the average load parameter at the nth moment, t = 1, 2,..., n - 1, and n is the number of time series;

[0018] The distributed power source power output time series is expressed as:

[0019] {P DG,t} = {p DG,1 , p DG , 2,..., p DG,n}

[0020] In the formula, P DG,t is the distributed power source power output time series, p DG,n is the distributed power source average power output value at the nth moment;

[0021] Based on the daily load time series and the distributed power generation output time series, calculate the net load value at each moment to form a net load time series data set, where the net load value in the net load time series data set is expressed as:

[0022] {P Nload,t} = {p Nload,1 , p Nload,2 ,..., p Nload,n}

[0023] In the formula, P Nload,t is the set of net load values, and p Nload,n is the net load value at the nth moment.

[0024] Furthermore, the calculation process of the net load value at each moment includes:

[0025] Subtract the distributed power generation output value at the corresponding moment in each time period from the average load parameter at each moment in each time period to obtain the net load value at each moment, where the calculation expression is:

[0026] {P Nload,t} = {P load,t} - {P DG,t}

[0027] In the formula, P Nload,t is the net load value at time t, P load,t is the average load parameter at time t, and P DG,t is the distributed power generation output value at time t.

[0028] Furthermore, the process of dividing the net load subsequence includes:

[0029] Judge the positive and negative situations of the net load values at each moment;

[0030] Divide the net load values with the same positive and negative situations and continuous in the net load time series data set into a net load subsequence until the net load time series data set is divided into several net load subsequences composed of multiple time periods, where the net load time series data set is expressed as:

[0031] {P Nload,t} = {P Nload,t1 , P Nload,t2 ,..., P Nload,ti}

[0032] In the formula, P Nload,t is the net load time series data set at time t, and P Nload,ti is the i-th net load subsequence, and each subsequence is composed of several net load time series points.

[0033] Further, it further includes:

[0034] Before calculating the Renyi entropy of each net load subsequence, perform normalization processing on the net load data in each net load subsequence.

[0035] Further, the expression of the normalization processing is:

[0036]

[0037] In the formula, y t,i is the normalized value of the i-th net load subsequence at time t, and p Nload,ti is the net load value of the i-th net load subsequence at time t.

[0038] Further, the calculation expression of the Renyi entropy of each net load subsequence is:

[0039]

[0040] In the formula, H α (Y t,i ) is the Renyi entropy of the i-th net load subsequence at time t, α is the adjustment parameter, and n i is the dimension of the i-th net load subsequence. Let the distribution law of Y t,i be {P}, p k =P{Y t,i =y t,i} and p k ∈[0,1], p k is the ratio of the net load value at time k in the i-th net load subsequence to the total net load value of the i-th net load subsequence, and y t,i is the i-th net load subsequence.

[0041] Further, the calculation expression of the source-load balance degree is:

[0042]

[0043] In the formula, Q bi is the source-load balance degree of the i-th net load subsequence, which is used to reflect the matching degree of the source-load data in time series, and H α (Y t,i ) is the Renyi entropy of the i-th net load subsequence at time t, and H stand is the Renyi entropy reference value of the net load subsequence.

[0044] The present invention also provides an electronic device, including: one or more processors; a memory; and one or more programs stored in the memory, where the one or more programs include instructions for executing the method for quantitatively evaluating the source-load balance degree based on the Renyi entropy theory as described above.

[0045] The present invention also provides a computer-readable storage medium, including one or more programs for execution by one or more processors of an electronic device, where the one or more programs include instructions for executing the method for quantitatively evaluating the source-load balance degree based on the Renyi entropy theory as described above.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] (1) The present invention divides the net load subsequences according to the positive and negative conditions of the net load values at each moment, considers the magnitude relationship between the load demand and the output, and can effectively quantify the information amount of the data in the time series distribution by using the Renyi entropy theory, and can accurately quantify the tracking and matching degree of the load to the output.

[0048] (2) The Renyi entropy of the present invention can flexibly capture the sequence characteristics of different time scales by adjusting the parameter α. This flexibility can more precisely characterize the discreteness of the net load, so as to accurately show the distribution balance of each net load subsequence, thereby improving the accuracy of the source-load balance degree quantification.

[0049] (3) The source-load balance degree index of the present invention can better guide the collaborative optimization among the power distribution network source-load-storage on the basis of accurately quantifying the tracking and matching degree of the load to the new energy output, and can intuitively characterize the source-load matching and balance situation.

[0050] (4) The source-load balance degree quantification index based on the Renyi entropy theory proposed by the present invention can be embedded into optimization models such as the power distribution network dispatching operation as a constraint condition or an objective function, and can assist the collaborative optimization and decision-making of the power distribution network source-load-storage on the premise of fully considering the influence of the source-load balance degree. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 is a schematic diagram of the method flow of the present invention;

[0052] Figure 2 is the daily load curve of the park and the power output curve of the distributed power source of the present invention;

[0053] Figure 3 is a schematic diagram of the extraction of the net load subsequence of the present invention;

[0054] Figure 4 is a schematic diagram of the source-load balance degree of the present invention;

[0055] Figure 5 Schematic diagram of the net load curve under the quantification model of the different-source load balance degree of the present invention Specific implementation manners

[0056] The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. This embodiment is implemented on the premise of the technical solution of the present invention, and gives the detailed implementation manners and specific operation processes, but the protection scope of the present invention is not limited to the following embodiments.

[0057] Embodiment 1

[0058] This embodiment provides a quantification evaluation method for the source-load balance degree based on the Renyi entropy theory. This method first collects the basic source-load operation data of the power grid. On this basis, the daily load curve of the power grid and the power output curve of distributed power sources are discretized to generate a net load time series composed of the power output of distributed power sources and load data. Further, it is judged whether the values of the net load at each moment are positive or negative, and the net load curve is divided into net load subsequences composed of several time periods. Finally, a quantification model for the source-load balance degree of the net load subsequence based on the Renyi entropy theory is established, and the source-load balance degree is calculated to effectively describe the matching degree of the source-load data in time series and intuitively represent the source-load matching and balance situation. Specifically, as Figure 1 shown, this method includes the following steps:

[0059] Step S1: Collect the daily load curve of the distribution network and the power output curve of distributed power sources.

[0060] The experimental example of the present invention selects a certain 10kV industrial park in a certain place for analysis, and the daily load curve and the power output curve of distributed power sources of this park are as Figure 2 shown.

[0061] Step S2: Discretize the daily load curve of the distribution network and the power output curve of distributed power sources to obtain their respective time series.

[0062] Based on the source-load operation data collected in step S1, the daily load curve of the distribution network and the power output curve of distributed power sources are discretized respectively. The daily load characteristic curve and the power output curve of distributed power sources are divided in units of 1 hour, and the average load parameters and the average power output values of distributed power sources in each time period are obtained to obtain the daily load time series and the power output time series of distributed power sources, as specifically shown below:

[0063] The daily load time series is:

[0064] {P load,t} = {p load,1 , p load,2 ,..., p load,n} (1) where: t = 1, 2, …, n - 1, n is the number of time series, and p load,n is the average load parameter at the nth moment.

[0065] The time series of distributed power output is:

[0066] {P DG,t} = {p DG,1 , p DG,2 , …, p DG,n} (2) where: t = 1, 2, …, n - 1, n is the number of time series, and p DG,n is the average power output value of the distributed power source at the nth moment.

[0067] Step S3: Further process the time series obtained by discretization to obtain the net load time series.

[0068] Further process the daily load time series and the distributed power output time series obtained in Step S2. Subtract the average power output value of the distributed power source at each corresponding moment from the average load parameter at each moment to obtain the net load value at each moment. Among them, the processing method is expressed as:

[0069] {P Nload,t} = {P load,t} - {P DG,t} (3)

[0070] From equations (1) and (2), the net load time series is:

[0071] {P Nload,t} = {p Nload,1 , p Nload,2 , …, p Nload,n} (4)

[0072] where: p Nload,n is the net load value at the nth moment.

[0073] Step S4: Judge the positive and negative situations of the net load values at each moment, and divide the net load curve into net load subsequences composed of several time periods.

[0074] Based on the net load time series generated in Step S3, by judging the positive and negative situations of the net load values p Nload,n at each moment, the net load values with the same and continuous values can be divided into a net load subsequence. Finally, the net load curve is divided into several net load subsequences composed of several time periods. The specific extraction schematic diagram is as Figure 3 shown. At this time, the net load time series can be expressed as:

[0075] {PNload,t} = {P Nload,t1 , P Nload,t2 ,..., P Nload,ti} (5) Wherein: PNload,ti represents the i-th net load time subsequence, and each subsequence is composed of a number of net load time points.

[0076] Step S5: Normalize the net load data of each subsequence.

[0077] Based on the net load subsequences extracted in step S4, normalize the net load data of each subsequence respectively. The normalization method can be expressed as:

[0078]

[0079] Wherein: y t,i represents the normalized value at time t of the i-th net load subsequence; p Nload,ti represents the net load value at time t of the i-th subsequence.

[0080] Step S6: Calculate the Renyi entropy of each normalized net load subsequence.

[0081] In recent years, in order to effectively quantify the information content in the time series distribution of data, the Renyi entropy theory has been widely adopted. Renyi entropy is a concept for measuring information uncertainty, originating from entropy in physics and used to describe the degree of disorder of a system. In information theory, Renyi entropy measures the uncertainty or randomness of information content. The higher the entropy of information, the greater the amount of information it contains and the stronger the ability to eliminate uncertainty. Through the power data transmission and processing technology based on Renyi entropy, the power system can significantly improve the efficiency and accuracy of data transmission, reduce data loss and errors during transmission, and thus improve the performance and reliability of the entire system. Therefore, the present invention uses Renyi entropy to perform quantitative analysis on the time series characteristics.

[0082] After the normalization processing in step 5, use Renyi entropy to quantitatively analyze the time series distribution characteristics of the net load subsequences. Through Renyi entropy, the information content in the time series distribution of data can be effectively quantified, and at the same time, the information value hidden in the power system data can be mined, providing a useful reference for improving the economic efficiency and low-carbon performance of the power system operation. Therefore, it is proposed to measure the dispersion degree of the net load values of each subsequence by Renyi entropy, and use this as a quantitative index for measuring the source-load balance degree. The normalized net load values can be used to calculate the Renyi entropy of the i-th net load subsequence. Denote the Renyi entropy of the i-th net load subsequence as H(Y t,i ), then:

[0083]

[0084] In the formula: n i is the dimension of the i-th net load subsequence. Let the distribution law of Y t,i be {P}, where p k = P{Y t,i = y t,i}, and p k ∈ [0, 1]. p k is the ratio of the net load value at time k in the i-th net load subsequence to the total net load value of the i-th net load subsequence. α is a regulation parameter, which can flexibly capture the sequence characteristics of different time scales. For example, when α = 1, it degenerates into Shannon entropy; when α = 2, it focuses on high-frequency fluctuations; when α approaches 0, it pays attention to low-frequency trends 2. This flexibility can more precisely characterize the complex fluctuation patterns of the net load and optimize the division of the output interval.

[0085] Step S7: Calculate the source-load balance degree by applying the Renyi entropy principle.

[0086] From the above formula and the physical meaning of the Renyi entropy, the smaller H(Y t,i ) is, the smaller the amount of information for measuring each net load subsequence is, the more concentrated y t,i is. At this time, the distributions of each net load subsequence are more balanced, the output curve (time series value) of the traditional generator set is relatively stable, and the matching degree of the source-load data in time series is higher; on the contrary, the larger H(Y t,i ) is, the more discrete y t,i is. At this time, the balance of the distributions of each net load subsequence is poor, the matching degree of the source-load data in time series is low, and it is not conducive to the conventional load to absorb distributed power sources. Therefore, on the basis of effectively characterizing the Renyi entropy of each net load subsequence, the source-load balance degree is defined as the matching degree of the distributed new energy output with strong uncertainty and the load power in time series during the construction of the new distribution network, that is, the aggregation level of the net load values in time series. Taking the Renyi entropy of each net load subsequence obtained in step 6 as a quantization index and substituting it into the specific quantization method of the source-load balance degree to obtain the source-load balance degree. The specific quantization method of the source-load balance degree can be expressed as:

[0087]

[0088] In the formula: Q bi is the source-load balance degree of the i-th net load subsequence, and H stand represents the Renyi entropy reference value of the net load subsequence. On the premise of ensuring the positive correlation between the value of Q bi and the source-load balance degree and the convenience of calculation, it is selected as 10 according to the maximum theoretical value of the Renyi entropy.

[0089] Through the source-load balance degree Qbi , which can effectively characterize the matching degree of source-load data in time series, specifically as Figure 4 shown. At the same time, from Figure 4 it can be seen that the larger the Q bi value, the closer the source-load coordination relationship is, the greater the balance degree between distributed power sources and conventional loads. At this time, the accommodation level of distributed power sources will be significantly improved, and the time series distribution of net load will be more concentrated at the same level, thereby making the output curve of traditional units smoother.

[0090] The method for quantitatively evaluating the source-load balance degree based on the Renyi entropy theory proposed by the present invention starts from the perspectives of energy use cleanliness and the smoothness of the output of traditional units. Based on the Renyi entropy theory, a time-segmented source-load balance degree index is proposed to effectively evaluate the source-load balance ability in each operation period. It is an effective way to solve the problem of optimal planning and operation of power systems containing renewable energy.

[0091] The method for quantitatively evaluating the source-load balance degree based on the Renyi entropy theory proposed by the present invention can, on the basis of accurately quantifying the tracking and matching degree of load to new energy output, achieve full accommodation of new energy, make the net load curve the smoothest, the peak-valley difference the lowest, and the standard deviation the smallest, so that the system operation economy and balance ability are at a higher level. At the same time, from Figure 5 it can be seen that Model 3 (i.e., the method of the present invention) using the method for quantitatively evaluating the source-load balance degree based on the Renyi entropy theory has the lowest total operation cost compared with Model 1 using the method for quantitatively evaluating the source-load balance based on the correlation coefficient and Model 2 using the method for quantitatively evaluating the source-load balance based on the Euclidean distance and the dynamic time warping method. It can achieve full accommodation of new energy, make the net load curve the smoothest, the peak-valley difference the lowest, and the standard deviation the smallest. While the operation costs of the other two models are relatively high and they fail to fully accommodate new energy. Therefore, the method proposed by the present invention enables deep interaction of various resources of source-load-storage, effectively improves the source-load balance ability and operation economy of the distribution network, and greatly improves the overall efficiency of the distribution network.

[0092] Embodiment 2

[0093] This embodiment provides an electronic device, including: one or more processors; a memory; and one or more programs stored in the memory, where the one or more programs include instructions for executing the method for quantitatively evaluating the source-load balance degree based on the Renyi entropy theory as described in Embodiment 1 above.

[0094] When the above functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs.

[0095] Those skilled in the art should understand that the embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) that contain computer-usable program codes. The solutions in the embodiments of the present invention can be implemented using various computer languages. For example, object-oriented programming languages such as Java and interpreted scripting languages such as JavaScript.

[0096] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0097] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device that implements the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0098] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are executed on the computer or other programmable apparatus to produce a computer-implemented process, and thus the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions specified in one process or multiple processes and / or blocks Figure 1 one process or multiple processes and / or blocks Figure 1 or steps for implementing the functions specified in multiple blocks.

[0099] Although the preferred embodiments of the present invention have been described, additional changes and modifications can be made by those skilled in the art once they learn of the basic creative concept. Therefore, the appended claims are intended to be construed to cover the preferred embodiments as well as all changes and modifications falling within the scope of the present invention.

[0100] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A source-load balance quantification evaluation method based on the Renyi entropy theory, characterized in that, It includes the following steps: Obtain the daily load curve of the distribution network and the power output curve of the distributed power source, and perform preprocessing to obtain a net load time series dataset; According to the positive and negative conditions of the net load values at each moment, divide the net load time series dataset into multiple net load subsequences; Calculate the Renyi entropy of each net load subsequence using the Renyi entropy theory; Calculate the source-load balance degree based on the Renyi entropy of each net load subsequence to complete the quantitative evaluation process of the source-load balance degree.

2. The source-load balance quantification evaluation method based on the Renyi entropy theory according to claim 1, characterized in that Use discrete sampling technology for the preprocessing, and the steps of the preprocessing include: Divide the daily load curve and the power output curve of the distributed power source in hours; According to the division results, calculate the average load parameter and the average power output value of the distributed power source for each time period to obtain the daily load time series and the power output time series of the distributed power source. Among them, the daily load time series is expressed as: {P load,t} = {p load,1 , p load,2 ,..., p load,n} Wherein, P load,t is the daily load time series, and p load,n is the average load parameter at the nth moment, where t = 1, 2, …, n - 1, and n is the number of time series The power output time series of the distributed power source is expressed as: {P DG,t} = {p DG,1 , p DG,2 ,..., p DG,n} Where, P DG,t is the time series of the distributed power source power output, and p DG,n is the average power output value of the distributed power source at the nth moment; Based on the daily load time series and the power output time series of the distributed power source, calculate the net load value at each moment to form a net load time series dataset, where the net load value in the net load time series dataset is expressed as: {P Nload,t} = {p Nload,1 , p Nload,2 ,..., p Nload,n} where P Nload,t is the set of net load values, and p Nload,n is the net load value at the nth moment.

3. The source-load balance quantization evaluation method based on Renyi entropy theory according to claim 2, wherein The calculation process of the net load value at each moment includes: Subtract the power output value of the distributed power source at the corresponding moment in each time period from the average load parameter at each moment in each time period to obtain the net load value at each moment, where the calculation expression is: {P Nload,t} = {P load,t} - {P DG,t} Where, P Nload,t is the net load value at time t, P load,t is the average load parameter at time t, and P DG,t is the distributed power output value at time t.

4. A method for quantifying and evaluating the source-load balance degree based on the Renyi entropy theory according to claim 1, characterized in that The division process of the net load subsequence includes: Judge the positive and negative conditions of the net load values at each moment; Divide the net load values with the same positive and negative conditions and continuous in the net load time series dataset into a net load subsequence until the net load time series dataset is divided into several net load subsequences composed of multiple time periods, where the net load time series dataset is expressed as: {P Nload,t} = {P Nload,t1 , P Nload,t2 ,..., P Nload,ti} where, P Nload,t is the net load time series data set at time t, and P Nload,ti is the i-th net load subsequence, and each subsequence is composed of a number of net load time series points.

5. The source-load balance quantification and evaluation method based on the Renyi entropy theory according to claim 1, characterized in that It also includes: Before calculating the Renyi entropy of each net load subsequence, perform standardization processing on the net load data in each net load subsequence.

6. The quantization evaluation method for source-load balance based on the Renyi entropy theory according to claim 5, characterized in that The expression of the standardization processing is: where y t,i is the normalization value of the i-th payload subsequence at time t, and p Nload,ti is the payload value of the i-th payload subsequence at time t.

7. A method for quantitatively evaluating the source-load balance degree based on the Renyi entropy theory according to claim 1, characterized in that The calculation expression of the Renyi entropy of each net load subsequence is: where, H α (Y t,i ) is the Renyi entropy of the i-th payload subsequence at time t, α is the adjustment parameter, n i is the dimension of the i-th payload subsequence. Let the distribution law of Y t,i be {P}, p k = P{Y t,i = y t,i} and p k ∈ [0, 1], p k is the ratio of the payload value at time k in the i-th payload subsequence to the total payload value of the i-th payload subsequence, and y t,i is the i-th payload subsequence.

8. A method for quantitatively evaluating the source-load balance degree based on the Renyi entropy theory according to claim 1, characterized in that, The calculation expression of the source-load balance degree is: where Q bi is the source-load balance degree of the i-th net load subsequence, which is used to reflect the matching degree of source-load data in time series, and H α (Y t,i ) is the Renyi entropy of the i-th net load subsequence at time t, and H stand is the Renyi entropy reference value of the net load subsequence.

9. An electronic device, characterized in that, It includes: One or more processors; A memory; And One or more programs stored in the memory, and the one or more programs include instructions for executing the source-load balance degree quantitative evaluation method based on the Renyi entropy theory as described in any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, It includes one or more programs executed by one or more processors of the power supply device, and the one or more programs include instructions for executing the source-load balance degree quantitative evaluation method based on the Renyi entropy theory as described in any one of claims 1-8.

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