A method for generating an annual time series of renewable energy output

Abstract

The application discloses a kind of renewable energy annual output time series generation method, belong to power system control technical field, the method, using Ito stochastic process to the current residual component and the current trend component in historical output time series by stationary calibration are modeled, obtain initial simulation trend component and initial simulation residual component;Further respectively to the inverse difference processing of both obtain target simulation trend component and target simulation residual component, finally reconstruct target output time series using the target simulation trend component, the target simulation residual component and the periodic component;The scheme can generate massive simulation curves by Ito stochastic process to the mathematical modeling of non-periodic component, while accurately reflecting its random characteristics and time series correlation, can accurately quantify the key features of renewable energy under long time scale, according to the above key features, the new energy consumption capacity of power grid can be accurately grasped.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of power system operation planning, and more particularly relates to a method for generating a full-year output time sequence of renewable energy. BACKGROUND

[0002] With the requirement of energy saving and carbon reduction proposed by the country, the proportion of renewable energy in the new power system is gradually increasing, and the significance of simulating the full-year time sequence operation of renewable energy also gradually emerges.

[0003] Most of the existing renewable energy scenario generation methods consider short-time scale power prediction in the day-ahead or intra-day, and lack appropriate modeling methods in the medium and long-term time scale, which cannot quantify the seasonal characteristics of renewable energy. Moreover, the existing time sequence operation simulation methods are mostly based on typical days, and the power system planning based thereon can only ensure the feasibility under the typical day, and may not consider some extreme scenarios. In fact, with the increasing proportion of renewable energy, the typical day is also changing. In addition, the traditional 8760-hour time sequence operation simulation method needs to add the time sequence constraints of the whole year, which will result in too large calculation amount and too low calculation efficiency.

[0004] Therefore, the existing modeling method is difficult to accurately quantify the key characteristics of renewable energy in the long time scale, which makes the power and energy balance of the power system face the risk of imbalance, is not conducive to the correct grasp of the new energy consumption capacity of the power grid, and cannot support the planning and development of large-scale power systems. SUMMARY

[0005] In view of the above defects or improvement needs of the prior art, the present application provides a method for generating a full-year output time sequence of renewable energy, which aims to solve the technical problem that the existing modeling method is difficult to accurately quantify the key characteristics of renewable energy in the long time scale.

[0006] To achieve the above-mentioned purpose, according to one aspect of the present application, a method for generating a full-year output time sequence of renewable energy is provided, comprising:

[0007] S1: decomposing the historical output time sequence of renewable energy to obtain a trend component, a residual component and a periodic component;

[0008] S2: performing stationarity check on the current residual component and the current trend component;

[0009] S3: if the stationarity check fails, performing difference processing on the current trend component and the current residual component to update the current trend component and the current residual component, and returning to S2 until the stationarity check is finally passed and S4 is entered;

[0010] S4: obtaining probability distribution of the current residual component and the current trend component respectively;

[0011] S5: applying the probability distribution in integral Fokker-Planck equation of Ito stochastic process, and modeling the current residual component and the current trend component by Ito stochastic process to obtain initial simulation trend component and initial simulation residual component;

[0012] S6: performing inverse differential processing on the initial simulation trend component and the initial simulation residual component to obtain target simulation trend component and target simulation residual component;

[0013] S7: reconstructing target output time series by using the target simulation trend component, the target simulation residual component and the periodic component.

[0014] In one of the embodiments, the S1 comprises:

[0015] The historical output time series of the renewable energy is decomposed by using an additive model or a multiplicative model to obtain the trend component, the residual component and a plurality of the periodic components.

[0016] In one of the embodiments, the S1 comprises:

[0017] S11: finding potential periodic components from the historical output time series;

[0018] S12: decoupling the potential periodic components according to a time series decomposition algorithm based on LOESS regression to obtain corresponding periodic components, and eliminating the periodic components from the current historical output time series to update the historical output time series;

[0019] S13: detecting whether there are other potential periodic components in the current historical output time series, if yes, returning to S12; otherwise, entering S14;

[0020] S14: decomposing the trend component and the residual component from the current historical output time series.

[0021] In one of the embodiments, the S2 comprises:

[0022] Performing unit root test on the current trend component and the current residual component in multiple ways;

[0023] The multiple ways of unit root test include ADF test and KPSS test, and if the multiple ways of unit root test are passed, it is considered to pass the stationarity check.

[0024] In one of the embodiments, the S5 comprises:

[0025] S51: modeling the current trend component using an Itô stochastic process to obtain a first mapping representing a relationship among a drift function, a diffusion function and a probability distribution of the current trend component, and bringing the probability distribution of the current trend component into the first mapping to obtain the drift function of the current trend component and the diffusion function of the current residual component, and reconstructing an initial simulated trend component using the two functions;

[0026] S52: modeling the current residual component using an Itô stochastic process to obtain a second mapping representing a relationship among a drift function, a diffusion function and a probability distribution of the current residual component, and bringing the probability distribution of the current residual component into the second mapping to obtain the drift function of the current residual component and the diffusion function of the current residual component, and reconstructing an initial simulated residual component using the two functions.

[0027] In one embodiment, the S51 comprises:

[0028] modeling the current trend component T(t) using T(t) = T(t-1) + U T (t) * Δt + V T (t) * ΔW(t) to obtain a first mapping representing a relationship among a drift function U T (t), a diffusion function V T (t) and a probability distribution p T (x, t) of the current trend component. ΔW(t) is a random factor;

[0029] bringing the probability distribution p T (x, t) of the current trend component into the first mapping to obtain the drift function of the current trend component and the diffusion function of the current residual component, and reconstructing an initial simulated trend component using the two functions.

[0030] In one embodiment, the S52 comprises:

[0031] modeling the current residual component R(t) using a formula R(t) = R(t-1) + U R (t) * Δt + V R (t) * ΔW(t) to obtain a second mapping representing a relationship among a drift function U R (t), a diffusion function V R (t) and a probability distribution p R (x, t) of the current residual component.

[0032] bringing the probability distribution p R(x, t) into the second mapping to obtain a drift function of the current residual component and a diffusion function of the current residual component, and reconstruct the initial simulated residual component using both.

[0033] In one embodiment, the S1 is followed by:

[0034] If the trend component and the residual component obtained by the S1 pass the stationarity check, the S4 and the S5 are executed, and the initial simulated trend component and the initial simulated residual component obtained by the S5 are first differentiated and then inverse differentiated to obtain a target simulated trend component and a target simulated residual component; finally, the target simulated trend component, the target simulated residual component and the periodic component are used to reconstruct a target output time series.

[0035] According to another aspect of the present application, a device for generating an annual output time series of a renewable energy source is provided, comprising:

[0036] a decomposition module configured to decompose a historical output time series of the renewable energy source to obtain a trend component, a residual component and a periodic component;

[0037] a check module configured to perform a stationarity check on a current residual component and a current trend component;

[0038] a loop module configured to, if the stationarity check fails, differentiate the current trend component and the current residual component to update the current trend component and the current residual component, and return to the check module until the stationarity check is passed and the device enters an acquisition module;

[0039] the acquisition module is configured to acquire a probability distribution of the current residual component and a probability distribution of the current trend component;

[0040] a modeling module configured to apply the probability distributions to an integral Fokker-Planck equation of an Itô stochastic process, and model the current residual component and the current trend component using the Itô stochastic process to obtain an initial simulated trend component and an initial simulated residual component;

[0041] a calculation module configured to inverse differentiate the initial simulated trend component and the initial simulated residual component to obtain a target simulated trend component and a target simulated residual component;

[0042] a reconstruction module configured to reconstruct a target output time series using the target simulated trend component, the target simulated residual component and the periodic component.

[0043] According to another aspect of the present application, there is provided a renewable energy management system comprising a memory and a processor, the memory storing a computer program, the processor implementing the steps of the method described above when executing the computer program.

[0044] According to another aspect of the present application, there is provided a computer readable storage medium having stored thereon a computer program, the computer program being executed by a processor to implement the steps of the method described above.

[0045] Overall, compared with the prior art, the above technical solutions conceived by the present application can achieve the following beneficial effects:

[0046] (1) The method for generating a renewable energy annual output time sequence provided by the present application uses an Itō stochastic process to model the current trend component and the current residual component in the historical output time sequence that pass the stationarity check, to obtain an initial simulated trend component and an initial simulated residual component; then inverse difference processing is performed on the two components to obtain a target simulated trend component and a target simulated residual component, and finally the target output time sequence is reconstructed using the target simulated trend component, the target simulated residual component and the periodic component; the present application can generate a large number of simulated curves by mathematically modeling the aperiodic component using the Itō stochastic process, while accurately reflecting its random characteristics and time sequence correlation, and can accurately quantify the key characteristics of renewable energy at a long time scale, so as to accurately grasp the new energy consumption capacity of the power grid and facilitate the planning and development of large-scale power systems.

[0047] (2) The present application uses an additive model or a multiplicative model to decompose the historical output time sequence, and uses the additive model or the multiplicative model to reconstruct the target output time sequence, which has low computational complexity and can improve the execution efficiency of the entire algorithm. Further, this method can also generate multiple periodic components, such as annual components, quarterly components, monthly components, and daily components, which facilitate accurate characterization of the key characteristics of renewable energy at a long time scale.

[0048] (3) The present application first eliminates some easily identified periodic components, reducing the data size and computational complexity of the current historical output time sequence, and then further determines whether there are other potential periodic components, and if so, decouples the potential periodic components according to the LOESS regression-based time sequence decomposition algorithm; this method can first identify as many periodic components as possible, and has low computational complexity and high algorithm execution efficiency.

[0049] (4) The present application performs unit root tests on the current trend component and the current residual component in multiple ways; this can improve the accuracy of stationarity check.

[0050] (5) The scheme adopts Ito random process to model the current trend component and the current residual component respectively, obtains the corresponding first mapping and second mapping, and brings the corresponding probability distribution into the two mappings, and finally reconstructs the initial simulation trend component and the initial simulation residual component; this method can accurately depict the characteristics of the residual component and the trend component.

[0051] (6) The scheme models the current trend component T(t) by using T(t)=T(t-1)+U T (t)*Δt+V T (t)*ΔW(t), and also considers random factors, so that the final obtained trend component is more accurate and close to the real application scenario.

[0052] (7) The scheme models the current residual component R(t) by using R(t)=R(t-1)+U R (t)*Δt+V R (t)*ΔW(t), and also considers random factors, so that the final obtained residual component is more accurate and close to the real application scenario.

[0053] (8) In the scheme, a special case is considered, the trend component and the residual component obtained by S1 can be directly checked for stationarity, and then the initial simulation trend component and the initial simulation residual component obtained by S4 and S5 are executed, but the initial simulation trend component and the initial simulation residual component have not been differentiated, so the initial simulation trend component and the initial simulation residual component are first differentiated, and then inverse differentiated to restore the time series in the simulation state, so as to reconstruct the target output time series with the periodic component. BRIEF DESCRIPTION OF DRAWINGS

[0054] Figure 1 is a flowchart of a renewable energy annual output time series generation method provided by an embodiment 1 of the present application;

[0055] Figure 2 is a flowchart of another renewable energy annual output time series generation method provided by an embodiment 1 of the present application;

[0056] Figure 3 is a probability distribution function diagram of a mixed Gaussian distribution model provided by an embodiment 1 of the present application;

[0057] Figure 4 is a comparison diagram of a differentiated time series generated by an Ito process and a differentiated time series obtained by differentiating an original time series, which is used by an embodiment 1 of the present application;

[0058] Figure 5 is a comparison diagram of a trend component generated by an Ito process and a trend component in an original time series, which is used by an embodiment 1 of the present application;

[0059] Figure 6 is a schematic diagram of different components obtained by decomposing a wind speed time series according to the generation method of the annual renewable energy output time series provided in Embodiment 1 of the present application;

[0060] Figure 7 is a schematic diagram of different components obtained by decomposing a solar radiation time series according to the generation method of the annual renewable energy output time series provided in Embodiment 1 of the present application. DETAILED DESCRIPTION

[0061] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and should not be used to limit the present application. In addition, the technical features involved in each embodiment of the present application described below can be combined with each other as long as they do not conflict with each other.

[0062] Embodiment 1

[0063] As shown in Figure 1 , the present embodiment provides a generation method of an annual renewable energy output time series, comprising: S1-S7.

[0064] Specifically, S1: decomposing a historical output time series of renewable energy to obtain a trend component, a residual component and a periodic component. S2: performing stationarity verification on the current residual component and the current trend component. S3: if the stationarity verification fails, performing difference processing on the current trend component and the current residual component to update the current trend component and the current residual component, and returning to S2 until the stationarity verification is finally passed and S4 is entered. S4: obtaining the probability distribution of the current residual component and the current trend component respectively. S5: applying the probability distribution in the integral Fokker-Planck equation of the Ito stochastic process, and modeling the current residual component and the current trend component using the Ito stochastic process to obtain an initial simulated trend component and an initial simulated residual component. S6: performing inverse difference processing on the initial simulated trend component and the initial simulated residual component to obtain a target simulated trend component and a target simulated residual component. S7: reconstructing a target output time series using the target simulated trend component, the target simulated residual component and the periodic component.

[0065] As an optional implementation, S1 is followed by: if the trend component and the residual component obtained by S1 pass the stationarity check, then performing S4 and S5, and performing differential processing on the initial simulated trend component and the initial simulated residual component obtained by S5, and then performing inverse differential processing to obtain a target simulated trend component and a target simulated residual component. Finally, the target simulated trend component, the target simulated residual component, and the periodic component are used to reconstruct a target output time series.

[0066] As an optional implementation, S1 includes: using an additive model or a multiplicative model to decompose the historical output time series of the renewable energy source to obtain a trend component, a residual component, and a plurality of periodic components.

[0067] The MSTL method (Multiple-STL) is improved from the STL method (a time series decomposition algorithm based on LOESS regression). After STL decomposition, the time series can be decomposed into a trend component, a periodic component, and a residual component. After MSTL decomposition, the time series can be decomposed into a trend component, a plurality of periodic components, and a residual component. The plurality of periodic components can include annual component, quarterly component, monthly component, and daily component, etc. The trend component and the residual component are non-periodic components. The STL method can only obtain one periodic component, while the MSTL method can obtain multiple periodic components.

[0068] It should be noted that for the MSTL method, the additive model and the multiplicative model are as follows:

[0069] X(t) = T(t) + S 1 (t) +... + S n (t) + R(t);

[0070] X(t) = T(t) × S 1 (t) ×... × S n (t) × R(t).

[0071] If the additive model is used for decomposition, S7 is to accumulate all components to form a complete annual time series of the renewable energy source, and modeling is completed. The accumulation formula is as follows: X(t) = T(t) + S 1 (t) + S 2 (t) + R(t). The entire process is repeated continuously to obtain a large number of time series curves.

[0072] As an optional implementation, S1 comprises: S11: finding potential periodic components from the historical output time series. S12: decoupling the potential periodic components according to a time series decomposition algorithm based on LOESS regression to obtain corresponding periodic components, and eliminating the periodic components from the current historical output time series to update the historical output time series. S13: detecting whether there are other potential periodic components in the current historical output time series, and returning to S12 if there are. Otherwise, S14 is entered. S14: decomposing the trend component and the residual component from the current historical output time series.

[0073] It should be noted that the STL method comprises an inner loop and an outer loop, and the steps of the inner loop are as follows:

[0074] (1) setting the initial value of the trend component and the initial value of the robust weight factor.

[0075] (2) subtracting the trend component from the complete time series to obtain the detrended time series X'(t), X'(t) = X(t)-T(t).

[0076] (3) under the initial value of the robust weight factor, performing LOESS regression on all periodic sequences in X'(t) to obtain temporary periodic components X T (t).

[0077] (4) performing low-pass filtering on the temporary periodic components X T (t) to eliminate the periodic characteristics therein to obtain low-pass components X L (t).

[0078] (5) performing residual removal processing on the smoothed periodic sequence to obtain the periodic component S(t), S(t) = X T (t)-X L (t).

[0079] (6) subtracting the periodic component from the original time series to obtain the time series T o (t) in the sequence that does not contain the periodic component, i.e., the preliminary trend component T o (t) = X(t)-S(t).

[0080] (7) performing LOESS smoothing on T o (t) to obtain the trend component T(t). The inner loop ends here to obtain the trend component and the periodic component.

[0081] The steps of the outer loop are as follows: subtracting the trend component and the periodic component from the original time series to obtain the residual component R(t) = X(t)-T(t)-S(t), and performing inspection on the size thereof.

[0082] If too large, adjustment is made and the inner loop is returned to, at which time the trend component and the robust weight factor have been updated, the inner loop steps are repeated to obtain a new trend component and a new residual component. If the size is acceptable, the trend component, the periodic component and the residual component can be outputted, and the decomposition is completed.

[0083] As an optional implementation, S2 comprises: performing multiple ways of unit root test on the current trend component and the current residual component. The multiple ways of unit root test comprise: ADF test (Augmented Dickey-Fuller test) and KPSS test (Kwiatkowski-Phillips-Schmidt-Shin test), and if the multiple ways of unit root test are passed, it is considered that the stationarity check is passed.

[0084] Before the trend component and the residual component are processed by using the Itô random process, the stationarity check needs to be performed on the time series. If the multiple ways of unit root test are not satisfied, it indicates that the time series is not stationary, and the time series needs to be differentiated to obtain a differentiated time series. If the differentiated time series is still not satisfied, the differentiation is performed until the stationarity check is satisfied. The differentiation formula is as follows: T'(t) = T(t) - T(t-1). If the original time series is differentiated during the stationarity check, the inverse differentiation process of the same order is applied to the differentiated time series in S6, and the original time series of the simulated non-periodic component, i.e., the trend component and the residual component, is obtained.

[0085] As an optional implementation, S5 comprises: S51: modeling the current trend component by using the Itô random process to obtain a first mapping representing the relationship among the drift function, the diffusion function and the probability distribution of the current trend component, bringing the probability distribution of the current trend component into the first mapping to obtain the drift function of the current trend component and the diffusion function of the current residual component, and reconstructing the initial simulated trend component by using the two functions. S52: modeling the current residual component by using the Itô random process to obtain a second mapping representing the relationship among the drift function, the diffusion function and the probability distribution of the current residual component, bringing the probability distribution of the current residual component into the second mapping to obtain the drift function of the current residual component and the diffusion function of the current residual component, and reconstructing the initial simulated residual component by using the two functions.

[0086] Since the probability distribution of the random variable needs to be obtained before the simulated time series is generated by using the Itô random process, the probability distribution of the trend component and the residual component is obtained by using a mixture Gaussian distribution model. The general formula of the probability density of the mixture Gaussian distribution model is as follows: By adjusting the parameters a j ,b j ,c j in it, different data can be fitted.

[0087] The periodic component is relatively easy to model, so only the Itô stochastic process is used to model the trend component and the residual component, which can simulate the random variation of the trend component and the residual component and retain the time correlation. The initial description of the Itô stochastic process is as follows.

[0088] The form of a typical Itô stochastic process is as follows: It can be described as the sum of an integral with respect to time and an integral with respect to Brownian motion. Where X(0) is the initial value, the drift function U(t) and the diffusion function V(t) are time-varying functions. The drift function U(t) reflects the drift of the random variable around the expected value, and the diffusion function V(t) reflects the influence of the random factor W(t) on the random variable, reflecting the net change of the random variable.

[0089] Rewrite the general form of the Itô stochastic process into the differential form as follows: dX(t) = U(t)dt + V(t)dW. In fact, a continuous curve of renewable energy output cannot be obtained, so rewrite the differential form into the discrete form as follows: X(t)-X(t-1) = U(t)*Δt + V(t)*ΔW(t); and after determining the probability distribution of the random variable p(x,t), the drift function U(t) and the diffusion function V(t) satisfy the Fokker-Planck equation as follows:

[0090]

[0091] Integrate the above equation to obtain the relationship between the drift function, the diffusion function, and the probability distribution of the random variable as follows:

[0092] As an optional implementation, S51 includes: modeling the current trend component T(t) by using T(t) = T(t-1) + U T (t)*Δt + V T (t)*ΔW(t) to obtain the first mapping representing the relationship between the drift function U T (t) of the current trend component, the diffusion function V T (t), and the probability distribution p T (x,t) of the current trend component. ΔW(t) is a random factor. The probability distribution p T (x,t) of the current trend component is brought into the first mapping to obtain the drift function of the current trend component and the diffusion function of the current residual component, and the initial simulated trend component is reconstructed by using the two.

[0093] Here, the probability distributions of the trend component and the residual component have been obtained by using the mixed Gaussian distribution model, and the difference of the sequence has passed the stationarity check, so the Itô stochastic process is used to model the trend component and the residual component. The differential equation is defined as follows:

[0094] Specifically, the discretized Itô stochastic process is applied to the trend component, and the following equation is obtained: T(t) = T(t-1) + U T (t) * Δt + V T (t) * ΔW(t); the drift function of the trend component is represented as: U T (t) = -θ T (T(t) - T avg ); the probability distribution of the trend component is fitted by using a mixture Gaussian distribution model:

[0095] The first mapping relationship among the drift function, the diffusion function, and the probability distribution of the trend component is as follows:

[0096] As an optional implementation, S52 comprises: modeling the current residual component R(t) by using the formula R(t) = R(t-1) + U R (t) * Δt + V R (t) * ΔW(t) to obtain the second mapping relationship among the drift function U R (t) of the current residual component, the diffusion function V R (t), and the probability distribution p R (x, t) of the current residual component. The probability distribution p R (x, t) of the current residual component is brought into the second mapping to obtain the drift function of the current residual component and the diffusion function of the current residual component, and the initial simulation residual component is reconstructed by using the two functions.

[0097] Specifically, the discretized Itô stochastic process is applied to the residual component, and the following equation is obtained: R(t) = R(t-1) + U R (t) * Δt + V R (t) * ΔW(t); the drift function of the residual component is represented as: U R (t) = -θ R (R(t) - R avg ); the probability distribution of the residual component is fitted by using a mixture Gaussian distribution model, and the following equation is obtained: The second mapping relationship among the drift function, the diffusion function, and the probability distribution of the residual component is as follows:

[0098] By solving the above differential equation set, the difference simulation trend component and the residual component can be generated.

[0099] The following describes the decomposition test of wind speed time series and photovoltaic radiation time series for a certain region using the method for generating annual renewable energy output time series provided by this invention. Two scenarios are set up to illustrate the effectiveness of this invention:

[0100] First scenario: Historical time series;

[0101] Second scenario: 1000 simulated time series are generated according to the method for generating annual renewable energy output time series proposed in this invention.

[0102] like Figure 2 The process shown decomposes the wind speed time series and photovoltaic radiation time series of a certain region. The method for generating the annual renewable energy output time series proposed in the second case specifically includes the following steps:

[0103] S1. Using the MSTL method, the historical time series of renewable energy is decomposed to obtain trend components, annual components, periodic components, and residual components.

[0104] S2. Perform stationarity checks on historical time series, namely the ADF test and the KPSS test.

[0105] S3. If these two tests are not satisfied, it indicates non-stationarity. The time series of the trend component and residual component need to be differencing to obtain the differencing time series. If the stationarity test is still not satisfied, differencing continues until the stationarity requirement is met. The differencing formula is as follows:

[0106] T′(t)=T(t)-T(t-1);

[0107] S4. The probability distributions of the trend component and the residual component are obtained by fitting a Gaussian mixture model. The general formula for the probability density of the Gaussian mixture model is as follows: By adjusting parameter a j ,b j ,c j It can be used to fit different types of data. Figure 3 This is a schematic diagram of the probability distribution function of a Gaussian mixture model.

[0108] S5. The Ito stochastic process is used to model the trend component and the residual component to generate the differential trend time series and residual time series. Figure 4 This is a comparison chart of the differential time series generated using the Ito process and the differential time series obtained by differentiating the original time series. Figure 5 This is a comparison chart of the trend components generated using the Itoh process and the trend components in the original time series.

[0109] S6, if the original time series is differentiated in the stationarity check, the same order of inverse differentiation process is applied to the differentiated time series here, that is, the original time series of the simulated trend component and residual component can be obtained.

[0110] S7, all components are accumulated to form a complete renewable energy annual time series, and modeling is completed. The accumulation formula is as follows: X(t) = T(t) + S 1 (t) + S 2 (t) + R(t), and the whole process is repeated to obtain a large number of time series curves.

[0111] The two statistics of mean and standard deviation are selected to verify the accuracy of the renewable energy annual output time series generation method proposed in the application in extracting the characteristics of the component. Through the above steps, 1000 simulated time series are generated in the second case. As can be seen from the calculation results in the following table, the mean and standard deviation of the original time series in the first case are 5.79 and 0.29, and the mean and standard deviation of the simulated time series in the second case are 5.84 and 0.23. It can be seen that the mean and variance obtained in the first case and the second case are almost the same, which proves the accuracy and effectiveness of the annual time series decomposition modeling method proposed in the application, and verifies the accurate characterization of the characteristics of the renewable energy time series by the method.

[0112] Statistical quantities First case Second case Mean 5.79 5.84 Standard deviation 0.29 0.23

[0113] wherein, Figure 6 is a schematic diagram of different components obtained by decomposing the wind speed time series based on the renewable energy annual output time series generation method; Figure 7 is a schematic diagram of different components obtained by decomposing the solar radiation time series based on the renewable energy annual output time series generation method, which further verifies that the renewable energy annual output time series generation method can accurately capture the characteristics of renewable energy and generate long-time-scale simulated time series similar to the original historical time series.

[0114] Embodiment 2

[0115] The embodiment provides a renewable energy annual output time series generation device, which comprises a decomposition module, a check module, a cycle module, an acquisition module, a modeling module, a calculation module and a reconstruction module.

[0116] The decomposition module is used for decomposing the historical output time series of renewable energy to obtain a trend component, a residual component and a periodic component.

[0117] The check module is used for checking the stationarity of the current residual component and the current trend component.

[0118] a cycle module for differentiating the current trend component and the current residual component to update the current trend component and the current residual component if the stationarity check fails, and returning to the check module until the stationarity check passes and entering the acquisition module.

[0119] an acquisition module for acquiring the probability distribution of the current residual component and the current trend component respectively.

[0120] a modeling module for applying the probability distribution in an integral Fokker-Planck equation of an Itô stochastic process, and modeling the current residual component and the current trend component by the Itô stochastic process to obtain an initial simulated trend component and an initial simulated residual component.

[0121] a calculation module for inversely differentiating the initial simulated trend component and the initial simulated residual component to obtain a target simulated trend component and a target simulated residual component.

[0122] a reconstruction module for reconstructing the target output time series by using the target simulated trend component, the target simulated residual component and the periodic component.

[0123] Embodiment 3

[0124] The embodiment provides a renewable energy management system, comprising a memory and a processor, the memory stores a computer program, and the processor implements the steps of the method when executing the computer program.

[0125] Embodiment 4

[0126] The embodiment provides a computer readable storage medium, which stores a computer program, and the computer program implements the steps of the method when executed by a processor.

[0127] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) containing computer-usable program code. The solutions in the embodiments of the present application can be implemented in various computer languages, such as object-oriented programming languages Java and interpreted scripting language JavaScript.

[0128] The present application is described in reference to the flowchart illustrations and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams.

[0129] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the functions specified in the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams.

[0130] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams.

[0131] While preferred embodiments of the application have been described, modifications and variations can be apparent to those skilled in the art once aware of the general underlying concepts. Therefore, it is intended that the present application be interpreted to include all modifications and alterations equivalent to those described above.

[0132] Obviously, numerous modifications and variations of the present application are possible in light of the above teachings. It is therefore to be understood that within the scope of the appended claims, the application can be practiced otherwise than as specifically described herein.

Claims

1. A method for generating a time series of annual renewable energy output, characterized in that, include: S1: Decompose the historical output time series of renewable energy to obtain trend components, residual components and periodic components; S2: Perform a stationarity check on the current residual components and the current trend components; S2 includes: performing unit root tests on the current trend component and the current residual component using multiple methods; the multiple unit root tests include: ADF test and KPSS test; if all multiple unit root tests pass, it is considered to have passed the stationarity check; S3: If the stationarity check fails, perform differential processing on the current trend component and the current residual component to update the current trend component and the current residual component, and return to S2 until the stationarity check is finally passed and enter S4. S4: Obtain the probability distributions of the current residual component and the current trend component; S5: Apply the probability distribution to the integral Fock-Planck equation of the Iton stochastic process, and use the Iton stochastic process to model the current residual component and the current trend component to obtain the initial simulated trend component and the initial simulated residual component. S6: Perform inverse differentiation processing on the initial simulated trend component and the initial simulated residual component to obtain the target simulated trend component and the target simulated residual component; S7: Reconstruct the target output time series using the target simulated trend component, the target simulated residual component, and the periodic component; The process after S1 includes: if the trend component and residual component obtained in S1 pass the stationarity check, then S4 and S5 are executed, and the initial simulated trend component and the initial simulated residual component obtained in S5 are first subjected to difference differentiation processing, and then subjected to inverse difference differentiation processing to obtain the target simulated trend component and the target simulated residual component; finally, the target simulated trend component, the target simulated residual component and the periodic component are used to reconstruct the target output time series.

2. The method for generating a time series of renewable energy output throughout the year as described in claim 1, characterized in that, S1 includes: The historical output time series of renewable energy is decomposed using an additive or multiplicative model to obtain the trend component, the residual component, and multiple periodic components.

3. The method for generating a time series of renewable energy output throughout the year as described in claim 2, characterized in that, S1 includes: S11: Identify potential periodic components from the historical power output time series; S12: Decouple the potential periodic components according to the time series decomposition algorithm based on LOESS regression to obtain the corresponding periodic components, and remove the periodic components from the current historical power output time series to update the historical power output time series. S13: Detect whether there are other potential periodic components in the current historical output time series. If they exist, return to S12; otherwise, proceed to S14. S14: Decompose the trend component and the residual component from the current historical output time series.

4. The method for generating a time series of renewable energy output throughout the year as described in claim 1, characterized in that, S5 includes: S51: The Ito stochastic process is used to model the current trend component to obtain a first mapping that characterizes the relationship between the drift function, diffusion function and probability distribution of the current trend component. The probability distribution of the current trend component is substituted into the first mapping to obtain the drift function of the current trend component and the diffusion function of the current residual component. The two are used to reconstruct the initial simulated trend component. S52: The current residual component is modeled using the Itō random process to obtain a second mapping that characterizes the relationship between the drift function, diffusion function and probability distribution of the current residual component. The probability distribution of the current residual component is then substituted into the second mapping to obtain the drift function and diffusion function of the current residual component. The initial simulation residual component is then reconstructed using these two functions.

5. The method for generating a time series of renewable energy output throughout the year as described in claim 4, characterized in that, S51 includes: use For the current trend component Modeling is performed to obtain the drift function that represents the current trend component. diffusion function and probability distribution The first mapping of the relationship among the three ; It is a random factor; The probability distribution of the current trend component Substituting the first mapping, we obtain the drift function of the current trend component and the diffusion function of the current residual component, and use them to reconstruct the initial simulated trend component.

6. The method for generating a time series of renewable energy output throughout the year as described in claim 4, characterized in that, S52 includes: Using formula For the current residual components Modeling is performed to obtain the drift function characterizing the current residual component. diffusion function and probability distribution The second mapping of the relationship among the three ; It is a random factor; The probability distribution of the current residual components Substituting the second mapping, we obtain the drift function and the spread function of the current residual component, and use them to reconstruct the initial simulation residual component.

7. A device for generating a time series of annual renewable energy output, characterized in that, A method for generating a time series of annual renewable energy output as described in any one of claims 1-6, comprising: The decomposition module is used to decompose the historical output time series of renewable energy to obtain trend components, residual components, and periodic components. The verification module is used to verify the stationarity of the current residual components and the current trend components. The loop module is used to perform differential processing on the current trend component and the current residual component to update the current trend component and the current residual component if the stationarity check fails, and then return to the check module until the stationarity check is finally passed and the module is entered. The acquisition module is used to acquire the probability distributions of the current residual component and the current trend component. The modeling module is used to apply the probability distribution to the integral Fokker-Planck equation of the Iton stochastic process, and to model the current residual component and the current trend component using the Iton stochastic process to obtain the initial simulated trend component and the initial simulated residual component. The calculation module is used to perform inverse differentiation processing on the initial simulated trend component and the initial simulated residual component to obtain the target simulated trend component and the target simulated residual component; The reconstruction module is used to reconstruct the target output time series using the target simulated trend component, the target simulated residual component, and the periodic component.

8. A renewable energy management system, comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

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