Short-term electricity price fluctuation risk assessment method and system based on multi-fractal theory, medium and processor

Abstract

The invention provides a short-term electricity price fluctuation risk assessment method based on a multi-fractal theory. The short-term electricity price fluctuation risk assessment method comprises the steps of S1, collecting electricity price sequence data and initializing a risk threshold V; s2, calculating a local Holder index and a multi-fractal spectrum of the electricity price sequence; s3, calculating the total fluctuation amplitude of the electricity price sequence and the total trend of the electricity price according to the local Holder index and the multi-fractal spectrum so as to explain the fluctuation characteristics of the multi-fractal spectrum for analyzing the potential risk of the electricity price fluctuation on the electricity market; s4, calculating a probability density function of the regression interval sequence under the action of the threshold value V, and solving the occurrence probability of events exceeding the threshold value V for each evaluation time period; and S5, evaluating a risk value VaR of electricity price fluctuation in a time period starting from the current time point based on the multi-fractal characteristic spectrum. By adopting the short-term electricity price fluctuation risk estimation method considering the multi-fractal characteristics, more accurate analysis can be carried out, so that electricity market participants are helped to manage financial risks brought by electricity price fluctuation.

Description

Technical Field

[0001] The present invention relates to the technical field of electricity price risk assessment, and in particular to a short-term electricity price fluctuation risk assessment method, system, medium and processor based on multifractal theory. Background Art

[0002] Among the many risk factors in the power market, highly volatile electricity prices have a major impact on financial risks. The Chinese invention patent application with application number CN201911157779.7 discloses a medium- and long-term power market risk assessment method based on machine learning, which collects power market risk-related data, system operation constraint data, and quotation data; based on the acquired data, a medium- and long-term power market risk assessment indicator system is established, and the degree of harm of typical risk events in the medium- and long-term power market is quantitatively analyzed, and divided according to the size of the total harm amount; the Delphi method is used to evaluate the acquired data, and the medium- and long-term power market risk assessment data is obtained, which can provide effective risk warning for market operators. However, the above method is less effective in short-term risk management scenarios such as spot markets and real-time balancing markets, and it is necessary to find a risk assessment method suitable for this specific scenario.

[0003] In view of this, a short-term electricity price fluctuation risk assessment method, system, medium and processor based on multifractal theory are needed. Summary of the invention

[0004] In view of the poor effectiveness of the prior art in short-term risk management scenarios such as the spot market and the real-time balancing market, the present invention provides a short-term electricity price fluctuation risk assessment method, system, medium and processor based on multifractal theory, which can apply multifractal theory to assess the financial risks brought by electricity price fluctuations, and adopt a modified return interval method considering multifractal characteristic parameters to estimate the risk value of electricity prices, which can effectively assess the risk of short-term electricity price fluctuations. The specific technical solution is as follows:

[0005] A short-term electricity price fluctuation risk assessment method based on multifractal theory includes:

[0006] S1: electricity price series data collection and risk threshold V initialization;

[0007] S2: Calculate the local Holder index and multifractal spectrum of the electricity price series;

[0008] S3: The overall fluctuation amplitude and overall trend of electricity price series are calculated based on the local Holder index and multifractal spectrum to explain the fluctuation characteristics of multifractal spectrum and to analyze the potential risks of electricity price fluctuation to the electricity market;

[0009] S4: Calculate the probability density function of the regression interval sequence under the threshold V, and obtain the probability of the event exceeding the threshold V for each evaluation period;

[0010] S5: Evaluate the VaR of electricity price fluctuations in the time period starting from the current time point based on the multi-fractal characteristic map.

[0011] Furthermore, the electricity price series data collection refers to collecting hourly electricity price time series: {X(t)}, t=1, 2, ..., N.

[0012] Furthermore, the risk threshold initialization includes the following steps:

[0013] Given a confidence level c;

[0014] Calculate the mean regression interval R based on the confidence level c v , the formula is as follows:

[0015] R v =1 / (1-c);

[0016] Set the initial threshold V based on knowledge and experience in the field;

[0017] The initial threshold V is used as the initial estimated risk value VaR for the next trading hour.

[0018] Furthermore, the calculation of the local Holder index and multifractal spectrum of the electricity price sequence includes the following steps:

[0019] S21: Divide the electricity price series into multiple normalized boxes of size δ, the formula is as follows:

[0020] δ = Δ / N;

[0021] S22: Calculate the average probability of box i after partitioning. The formula is as follows:

[0022]

[0023] S23: Calculate the partition function based on the average probability, the formula is as follows:

[0024]

[0025] M=δ -1 ;

[0026] S24: Plot lnS q The (δ)-lnδ double logarithmic curve is used to calculate the slope of the linear part of the curve to obtain the scaling function. The local Holder index is calculated based on the scaling function. The formula is as follows:

[0027]

[0028] S25: Calculate the multifractal spectrum using Legendre transform, the formula is as follows:

[0029]

[0030] In the above formula, Δ∈[1, N], N is divisible by all Δ; N is the number of electricity price sequence; X(i k ) represents the kth electricity price in the ith box; P i (δ) is the average probability of box i; S q (δ) is the partition function; q is the moment order coefficient, q∈(-∞, +∞); τ(q) is the scaling function; α is the local Holder index; f(α) is the multifractal spectrum.

[0031] Furthermore, the overall fluctuation range and overall trend of the electricity price sequence are calculated based on the local Holder index and the multifractal spectrum, and the formula is as follows:

[0032] Δα=α max -α min ;

[0033]

[0034] In the above formula, Δα is the overall fluctuation range of the electricity price series; α min and α max They represent the maximum and minimum values ​​of the local Holder index α respectively; α0 represents the value of α when q=0; S α is the overall trend of electricity prices during the estimation period.

[0035] Furthermore, the calculation of the probability density function of the regression interval sequence under the action of the threshold value V, and obtaining the probability of occurrence of an event exceeding the threshold value V for each evaluation period, includes the following steps:

[0036] The probability density function calculation formula of the electricity price series is as follows:

[0037]

[0038] It can be obtained that under the action of threshold V, {r i The probability density function P of v (r) obeys the distribution (r / R V ) -θ(V) :

[0039] P v (r) - (r / R V ) -θ(V) ;

[0040] Calculate the probability of an event exceeding the threshold V, the formula is as follows:

[0041]

[0042] In the above formula, N V For a regression interval sequence {r i}, with an interval of r i Defined as two adjacent extreme events X(t j1 ) and X(t j2 ) exceeds the threshold V, i = 1, 2, ..., N V ; P(X) is the probability density function of the electricity price sequence X(t); θ(V) is the power exponent of the threshold V; P v (r) is the probability density function of the regression interval sequence under the threshold V; B v (Δt l ,Δt) is the probability of an event exceeding the threshold V in each evaluation period; Δt is the evaluation period; Δt l is the time point of the last event exceeding the threshold V.

[0043] Furthermore, the method of evaluating the risk value VaR of electricity price fluctuations in a time period starting from the current time point based on the multifractal characteristic graph includes the following steps:

[0044] S51: Given the permissible error of the VaR confidence level, if |B V (Δt l ,Δt)-(1-c)|≤ξ, then the threshold V at this time can be considered as the risk value VaR at this confidence level; if |B V (Δt l , Δt)-(1-c)|>ξ, proceed to step S52;

[0045] S52: If this step is performed for the first time, calculate the reference value S gk Then enter step S53, the reference value S gk The calculation formula is as follows:

[0046] S gk =sgn(B V (Δt l ,Δt)-(1-c));

[0047] If this step is not performed for the first time, calculate the reference value S gk and the reference value S when first calculated g0 Compare, if they are the same, proceed to step S53, otherwise the current threshold V is regarded as the risk value VaR;

[0048] S53: If B v (Δt l,Δt)>(1-c), then raise the threshold V and return to step S2; if B v (Δt l ,Δt)<(1-c), then lower the threshold V and return to step S2;

[0049] In the above formula, S g0 For the first calculation of the reference value S gk ; k is the number of times this step is performed.

[0050] A short-term electricity price fluctuation risk assessment system based on multifractal theory is applied to the short-term electricity price fluctuation risk assessment method based on multifractal theory described above, comprising:

[0051] A collection module, which is used for collecting electricity price series data and initializing the risk threshold V;

[0052] A first calculation module, which is used to calculate the local Holder index and multifractal spectrum of the electricity price series;

[0053] The second calculation module is used to calculate the overall fluctuation amplitude and overall trend of the electricity price series according to the local Holder index and the multifractal spectrum, so as to explain the fluctuation characteristics of the multifractal spectrum and analyze the potential risks of electricity price fluctuations to the electricity market;

[0054] A third calculation module is used to calculate the probability density function of the regression interval sequence under the action of the threshold value V, and to obtain the probability of occurrence of an event exceeding the threshold value V for each evaluation period;

[0055] The evaluation module is used to evaluate the risk value VaR of electricity price fluctuations in a time period starting from the current time point based on the multi-fractal characteristic map.

[0056] A computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the above-mentioned short-term electricity price fluctuation risk assessment method based on multifractal theory.

[0057] A processor is used to run a program, wherein the program executes the above-mentioned short-term electricity price fluctuation risk assessment method based on multifractal theory when running.

[0058] Compared with the prior art, the present invention has the following beneficial effects:

[0059] 1. The volatility risk estimation method based on multifractal theory proposed in this paper is more suitable for short-term electricity price fluctuations and has significant advantages over the VaR estimation method based on Gaussian distribution and another widely used historical data estimation method. Therefore, by adopting the short-term electricity price volatility risk estimation method considering multifractal characteristics, more accurate analysis can be carried out to help electricity market participants manage the financial risks brought about by electricity price fluctuations. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for the specific embodiments or the description of the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn according to the actual scale.

[0061] Figure 1 This is a flow chart of a short-term electricity price fluctuation risk assessment method based on multifractal theory. DETAILED DESCRIPTION

[0062] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0063] It should be understood that when used in this specification and the appended claims, the terms "include" and "comprises" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0064] It should also be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0065] It should be further understood that the term "and / or" used in the present description and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0066] Implementation Column 1

[0067] like Figure 1As shown in FIG. 1 , a short-term electricity price fluctuation risk assessment method based on multifractal theory includes:

[0068] S1: Electricity price series data collection and risk threshold V initialization.

[0069] Furthermore, the electricity price series data collection refers to collecting hourly electricity price time series: {X(t)}, t=1, 2, ..., N.

[0070] Furthermore, the risk threshold initialization includes the following steps:

[0071] S11: Given a confidence level c; common confidence levels include: 90% confidence level, which means that we have 90% confidence that the true parameter value is within the calculated confidence interval. 95% confidence level, which is one of the most common confidence levels, means that we have 95% confidence that the true parameter value is within the calculated confidence interval. 99% confidence level, which means that we have 99% confidence that the true parameter value is within the calculated confidence interval. This confidence level is higher and may be used for some research or decision-making that requires a higher degree of confidence. According to the concept of return interval r i (The return interval refers to the time interval between two adjacent sequence values ​​exceeding the threshold V under the premise of a given threshold V). The estimated risk value VaR is equivalent to the threshold V.

[0072] S12: Calculate the mean regression interval R based on the confidence level c v , the formula is as follows:

[0073] R v =1 / (1-c)

[0074] S13: Set an initial threshold V based on domain knowledge and experience.

[0075] S14: Use the initial threshold V as the initial estimated risk value VaR for the next trading hour.

[0076] S2: Calculate the local Holder index and multifractal spectrum of the electricity price series.

[0077] Furthermore, the calculation of the local Holder index and multifractal spectrum of the electricity price sequence refers to using the box dimension method to calculate the local Holder index and multifractal spectrum of the electricity price sequence {X(t)}, t=1, 2, ..., N, including the following steps:

[0078] S21: Divide the electricity price series into multiple normalized boxes of size δ, the formula is as follows:

[0079] δ = Δ / N;

[0080] S22: Calculate the average probability of box i after partitioning. The formula is as follows:

[0081]

[0082] S23: Calculate the partition function based on the average probability, the formula is as follows:

[0083]

[0084] M=δ -1 ;

[0085] S24: Calculate the scaling function τ(q) and the local Holder The exponent α is the characteristic parameter of the multifractal process. From the multifractal theory, we know that P i (δ) is subject to the distribution δ α , that is, P i (δ) - δ. Partition function S q (δ) follows the distribution δ τ(q) , that is, S q (δ) - δ τ(q) Therefore, by plotting lnS q (δ)-lnδ double logarithmic curve, calculate the slope of the linear part of the curve to get the scaling function τ(q), and calculate the local Holder index α according to the scaling function τ(q), the formula is as follows:

[0086]

[0087] S25: Calculate the multifractal spectrum f(α) using Legendre transform, the formula is as follows:

[0088]

[0089] In the above formula, Δ∈[1, N], N is divisible by all Δ; N is the number of electricity price sequence; X(i k ) represents the kth electricity price in the i-th box, i = 1, 2, ... M; P i (δ) is the average probability of box i; S q (δ) is the partition function; q is the moment order coefficient, q∈(-∞, +∞); τ(q) is the scaling function; α is the local Holder index; f(α) is the multifractal spectrum.

[0090] S3: Calculate the overall fluctuation amplitude Δα of the electricity price series and the overall trend S of the electricity price based on the local Holder index and multifractal spectrum α, to explain the multifractal spectrum fluctuation characteristics, and to analyze the potential risks of electricity price fluctuations to the electricity market. The larger the overall fluctuation amplitude Δα of the electricity price series, the more drastic the fluctuation of the electricity price data. If the electricity price tends to rise, then S α >1; otherwise S α <1.

[0091] Furthermore, the overall fluctuation amplitude Δα of the electricity price sequence and the overall trend S of the electricity price are calculated based on the local Holder index and the multifractal spectrum. α , the formula is as follows:

[0092] Δα=α max -α min ;

[0093]

[0094] In the above formula, Δα is the overall fluctuation range of the electricity price series; α min and α max They represent the maximum and minimum values ​​of the local Holder index α respectively; α0 represents the value of α when q=0; S α is the overall trend of electricity prices during the estimation period.

[0095] S4: Calculate the probability density function of the regression interval sequence under the threshold V, and find the probability of the event exceeding the threshold V for each evaluation period. This step estimates the description θ(V) of the threshold V, and the power exponent θ(V) is a monotonically decreasing function of the threshold V, and θ(V)>1.

[0096] Furthermore, the calculation of the probability density function of the regression interval sequence under the action of the threshold value V, and obtaining the probability of occurrence of an event exceeding the threshold value V for each evaluation period, includes the following steps:

[0097] First, according to the mean regression interval R v Define the probability density function P(X) for calculating the electricity price sequence X(t). The calculation formula for the probability density function of the electricity price sequence is as follows:

[0098]

[0099] Since the original electricity price data sequence has multi-fractal characteristics, the threshold V i The probability density function P of v (r) obeys the distribution (r / R V ) -θ(V) , from which we get θ(V):

[0100] P v (r) - (r / R V ) -θ(V) ;

[0101] For each evaluation period, calculate the probability B of an event exceeding the threshold V v (Δt l ,Δt), the formula is as follows:

[0102] Let the current time be t c , the last time point that exceeds the threshold V (i.e. another extreme event occurs) is Δt l , definition B v (Δt l ,Δt) is the time from t c The probability that the sequence value exceeds the threshold V within the next Δt period.

[0103]

[0104] Compare Δt l The above formula can be simplified as follows: when Δt is much smaller than Δt l When (Δt<<Δt l ), simplified as follows:

[0105]

[0106] When Δt is much smaller than Δt l When , it is simplified as follows:

[0107]

[0108] Using the indicators Δα and S α Correct the above formula. After correction based on the multi-fractal characteristics of the electricity price time series, if the time series fluctuates more violently or tends to rise, the probability of extreme events increases, and vice versa. The corrected formula is as follows:

[0109]

[0110] In the above formula, N V For a regression interval sequence {r i}, for the electricity price sequence {X(t)}, t = 1, 2, ..., N, the interval r is returned i Defined as two adjacent extreme events X(t j1 ) and X(t j2 ) exceeds the threshold V, i = 1, 2, ..., N V ; P(X) is the probability density function of the electricity price sequence X(t); θ(V) is the power exponent of the threshold V; P v (r) is the probability density function of the regression interval sequence under the threshold V; B v (Δt l,Δt) is the probability of an event exceeding the threshold V in each evaluation period; Δt is the evaluation period; Δt l is the time point of the last event exceeding the threshold V.

[0111] S5: Evaluate the VaR of electricity price fluctuations in the time period starting from the current time point based on the multi-fractal characteristic map.

[0112] Furthermore, the method of evaluating the risk value VaR of electricity price fluctuations in a time period starting from the current time point based on the multifractal characteristic graph includes the following steps:

[0113] S51: Given the permissible error of the VaR confidence level, if |B V (Δt l ,Δt)-(1-c)|≤ξ, then the threshold V at this time can be considered as the risk value VaR at this confidence level; if |B V (Δt l , Δt)-(1-c)|>ξ, proceed to step S52;

[0114] S52: If this step is performed for the first time, calculate the reference value S gk Then enter step S53, the reference value S gk The calculation formula is as follows:

[0115] S gk =sgn(B V (Δt l ,Δt)-(1-c)); reference value S gk The symbolic function sgn is used for calculation. When it returns 1, it means that the probability of an extreme event is greater than 1-c; k is the number of times the step is performed, and the first step is counted as S g0 ;

[0116] If this step is not performed for the first time, calculate the reference value S gk and the reference value S when first calculated g0 Compare, if they are the same, proceed to step S53, otherwise the current threshold V is regarded as the risk value VaR;

[0117] S53: If B v (Δt l ,Δt)>(1-c), then raise the threshold V and return to step S2; if B v (Δt l ,Δt)<(1-c), then lower the threshold V and return to step S2; the amount of increase or decrease is ΔV; the initial modification of ΔV can be set to 50% of the threshold V, and each subsequent change of ΔV is 50% of the previous ΔV;

[0118] In the above formula, S g0For the first calculation of the reference value S gk ; k is the number of times this step is performed.

[0119] Taking the real-time electricity price data of a relatively mature local electricity market from June 1, 2007 to December 31, 2011 as an example, the risk value VaR caused by electricity price fluctuations is calculated using the method proposed in this application.

[0120] The statistical data of the estimation results are shown in Table 1. At the confidence level of c = 99 and 95%, the s failure rate of the real-time power market risk value VaR estimated in 2012 is slightly lower than the theoretical s failure rate. Therefore, the effectiveness of the proposed method in short-term VaR estimation has been verified.

[0121] Table 1

[0122] Confidence Level Sample size Estimation failure statistics Failure Rate % Theoretical failure rate % C＝99％ 8784 70 0.8 1.0 C＝95％ 8784 412 4.69 5.0

[0123] Table 2

[0124]

[0125] The VaR calculated based on Gaussian distribution and the VaR calculated based on historical data are compared and analyzed, as shown in Table 2 above. The average value of the estimated value of the VaR series of electricity price fluctuations is represented by V. The average estimation error of the difference series is recorded as A. The standard deviation of the error series is represented by D. The total estimated error of the fault series is shown as F.

[0126] Both the Gaussian distribution-based and historical data-based methods have a higher estimation failure rate than the proposed method based on multifractal theory. In addition, the estimation deviation shows that at c = 95%, the results obtained by VaR based on Gaussian distribution are significantly more accurate than those obtained by VaR based on historical data, while the results obtained by VaR based on historical data at c = 99% show a significant advantage. However, neither of these two methods is superior to the proposed method based on multifractal theory in terms of validity and estimation accuracy. Both the Gaussian distribution-based and historical data-based methods rely on the statistical parameters of historical electricity price data and are more suitable for long-term situations. Therefore, short-term electricity price fluctuations and fluctuation characteristics will be obscured by the overall statistical data and cannot effectively explain the volatility of electricity prices in short-term situations. The volatility risk estimation method based on multifractal theory proposed in this application is more suitable for short-term electricity price fluctuations and has significant advantages over the VaR estimation method based on Gaussian distribution and another widely used historical data estimation method. Therefore, by adopting a short-term electricity price fluctuation risk estimation method considering multifractal characteristics, a more accurate analysis can be performed to help electricity market participants manage the financial risks brought about by electricity price fluctuations.

[0127] Implementation column 2

[0128] A short-term electricity price fluctuation risk assessment system based on multifractal theory is applied to the short-term electricity price fluctuation risk assessment method based on multifractal theory described above, comprising:

[0129] A collection module, which is used for collecting electricity price series data and initializing the risk threshold V;

[0130] A first calculation module, which is used to calculate the local Holder index and multifractal spectrum of the electricity price series;

[0131] The second calculation module is used to calculate the overall fluctuation amplitude and overall trend of the electricity price series according to the local Holder index and the multifractal spectrum, so as to explain the fluctuation characteristics of the multifractal spectrum and analyze the potential risks of electricity price fluctuations to the electricity market;

[0132] A third calculation module is used to calculate the probability density function of the regression interval sequence under the action of the threshold value V, and to obtain the probability of occurrence of an event exceeding the threshold value V for each evaluation period;

[0133] The evaluation module is used to evaluate the risk value VaR of electricity price fluctuations in a time period starting from the current time point based on the multi-fractal characteristic map.

[0134] Implementation Column 3

[0135] A computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the above-mentioned short-term electricity price fluctuation risk assessment method based on multifractal theory.

[0136] Implementation List 4

[0137] A processor is used to run a program, wherein the program executes the above-mentioned short-term electricity price fluctuation risk assessment method based on multifractal theory when running.

[0138] The present application provides a short-term electricity price fluctuation risk assessment method based on multifractal theory, including: S1: electricity price sequence data collection and risk threshold V initialization; S2: calculation of the local Holder index and multifractal spectrum of the electricity price sequence; S3: calculation of the overall fluctuation amplitude and overall trend of the electricity price sequence based on the local Holder index and the multifractal spectrum to explain the multifractal spectrum fluctuation characteristics, which is used to analyze the potential risk of electricity price fluctuations to the electricity market; S4: calculation of the probability density function of the regression interval sequence under the threshold V, and the probability of occurrence of events exceeding the threshold V for each evaluation period; S5: evaluation of the risk value VaR of electricity price fluctuations in the time period starting from the current time point based on the multifractal characteristic spectrum. The volatility risk estimation method based on multifractal theory proposed in this paper is more suitable for short-term electricity price fluctuations, and has significant advantages over the VaR estimation method based on Gaussian distribution and another widely used historical data estimation method. Therefore, by adopting a short-term electricity price fluctuation risk estimation method considering multifractal characteristics, a more accurate analysis can be performed to help electricity market participants manage the financial risks brought about by electricity price fluctuations.

[0139] Those of ordinary skill in the art will appreciate that the units of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition of each example has been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0140] In the embodiments provided by the present invention, it should be understood that the division of units is only a logical function division, and there may be other division methods in actual implementation, for example, multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored, etc.

[0141] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0142] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it 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 all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-0nlyMemory), random access memory (RAM, RandomAccessMemory), mobile hard disk, magnetic disk or optical disk, etc., which can store program code.

[0143] 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 replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A short-term electricity price fluctuation risk assessment method based on multifractal theory, characterized in that: include: S1: Electricity price series data collection and risk threshold V initialization; S2: Calculate the local Holder index and multifractal spectrum of the electricity price series; S3: The overall fluctuation amplitude and overall trend of electricity price series are calculated based on the local Holder index and multifractal spectrum to explain the fluctuation characteristics of multifractal spectrum and to analyze the potential risks of electricity price fluctuation to the electricity market; S4: Calculate the probability density function of the regression interval sequence under the threshold V, and obtain the probability of the event exceeding the threshold V for each evaluation period; S5: Evaluate the VaR of electricity price fluctuations in the time period starting from the current time point based on the multi-fractal characteristic map.

2. The short-term electricity price fluctuation risk assessment method based on multifractal theory according to claim 1 is characterized in that: The electricity price series data collection refers to collecting hourly electricity price time series: {X(t)}, t=1, 2, ..., N.

3. The short-term electricity price fluctuation risk assessment method based on multifractal theory according to claim 1 is characterized in that: The risk threshold initialization comprises the following steps: Given a confidence level c; Calculate the mean regression interval R based on the confidence level c v , the formula is as follows: R v =1 / (1-c); Set the initial threshold V based on knowledge and experience in the field; The initial threshold V is used as the initial estimated value at risk VaR for the next trading hour.

4. The short-term electricity price fluctuation risk assessment method based on multifractal theory according to claim 1 is characterized in that: The method of calculating the local Holder index and multifractal spectrum of the electricity price sequence comprises the following steps: S21: Divide the electricity price series into multiple normalized boxes of size δ, the formula is as follows: δ = Δ / N; S22: Calculate the average probability of box i after partitioning. The formula is as follows: S23: Calculate the partition function based on the average probability, the formula is as follows: M=δ -1 ; S24: Plot lnS q The (δ)-lnδ double logarithmic curve is used to calculate the slope of the linear part of the curve to obtain the scaling function. The local Holder index is calculated based on the scaling function. The formula is as follows: S25: Calculate the multifractal spectrum using Legendre transform, the formula is as follows: In the above formula, Δ∈[1, N], N is divisible by all Δ; N is the number of electricity price sequence; X(i k ) represents the kth electricity price in the ith box; P i (δ) is the average probability of box i; S q (δ) is the partition function; q is the moment order coefficient, q∈(-∞, +∞); τ(q) is the scaling function; α is the local Holder index; f(α) is the multifractal spectrum.

5. The short-term electricity price fluctuation risk assessment method based on multifractal theory according to claim 4 is characterized in that: The overall fluctuation range and overall trend of electricity price sequence are calculated based on the local Holder index and multifractal spectrum. The formula is as follows: Dα=α max -a min ; In the above formula, Δα is the overall fluctuation range of the electricity price series; α min and α max They represent the maximum and minimum values ​​of the local Holder index α respectively; α0 represents the value of α when q=0; S α is the overall trend of electricity prices during the estimation period.

6. The short-term electricity price fluctuation risk assessment method based on multifractal theory according to claim 5 is characterized in that: The method of calculating the probability density function of the regression interval sequence under the action of the threshold value V and obtaining the probability of occurrence of an event exceeding the threshold value V for each evaluation period includes the following steps: The probability density function calculation formula of the electricity price series is as follows: It can be obtained that under the action of threshold V, {r i The probability density function P of v (r) obeys the distribution (r / R V ) -θ(V) : P v (r) - (r / R V ) -θ(V) ; Calculate the probability of an event exceeding the threshold V, the formula is as follows: In the above formula, N V is a regression interval sequence {r i }, with an interval of r i Defined as two adjacent extreme events X(t j1 ) and X(t j2 ) exceeds the threshold V, i = 1, 2, ..., N V ; P(X) is the probability density function of the electricity price sequence X(t); θ(V) is the power exponent of the threshold V; P v (r) is the probability density function of the regression interval sequence under the threshold V; B v (Δt l ,Δt) is the probability of an event exceeding the threshold V in each evaluation period; Δt is the evaluation period; Δt l is the time point of the last event exceeding the threshold V.

7. The short-term electricity price fluctuation risk assessment method based on multifractal theory according to claim 6 is characterized in that: The method of evaluating the risk value VaR of electricity price fluctuations in a time period starting from the current time point based on the multifractal characteristic graph includes the following steps: S51: Given the permissible error of the VaR confidence level, if |B V (Δt l ,Δt)-(1-c)|≤ξ, then the threshold V at this time can be considered as the risk value VaR at this confidence level; if |B V (Δt l , Δt)-(1-c)|>ξ, proceed to step S52; S52: If this step is performed for the first time, calculate the reference value S gk Then enter step S53, the reference value S gk The calculation formula is as follows: S gk =sgn(B V (Δt l ,Δt)-(1-c)); If this step is not performed for the first time, calculate the reference value S gk and the reference value S when first calculated g0 Compare, if they are the same, proceed to step S53, otherwise the current threshold V is regarded as the risk value VaR; S53: If B v (Δt l ,Δt)>(1-c), then raise the threshold V and return to step S2; if B v (Δt l ,Δt)<(1-c), then lower the threshold V and return to step S2; In the above formula, S g0 For the first calculation of the reference value S gk ; k is the number of times this step is performed.

8. A short-term electricity price fluctuation risk assessment system based on multifractal theory, characterized in that: The short-term electricity price fluctuation risk assessment method based on multifractal theory applied to any one of claims 1 to 7 comprises: A collection module, which is used for collecting electricity price series data and initializing the risk threshold V; A first calculation module, which is used to calculate the local Holder index and multifractal spectrum of the electricity price series; The second calculation module is used to calculate the overall fluctuation amplitude and overall trend of the electricity price series according to the local Holder index and the multifractal spectrum, so as to explain the fluctuation characteristics of the multifractal spectrum and analyze the potential risks of electricity price fluctuations to the electricity market; A third calculation module is used to calculate the probability density function of the regression interval sequence under the action of the threshold value V, and to obtain the probability of occurrence of an event exceeding the threshold value V for each evaluation period; The evaluation module is used to evaluate the risk value VaR of electricity price fluctuations in a time period starting from the current time point based on the multi-fractal characteristic map.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the short-term electricity price fluctuation risk assessment method based on multifractal theory as described in any one of claims 1 to 7.

10. A processor, characterized in that: The processor is used to run a program, wherein the program, when running, executes the short-term electricity price fluctuation risk assessment method based on multifractal theory as described in any one of claims 1 to 7.

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