A statistically feasible distributed renewable energy joint opportunity-constrained scheduling method, computer-readable storage medium, and program product

Abstract

The present invention proposes a statistically feasible distributed renewable energy joint opportunity constrained scheduling method, a computer-readable storage medium and a program product, including: establishing a distributed renewable energy joint opportunity constrained scheduling model, constructing a statistically feasible optimization scheduling model, and constructing an uncertainty set and a secondary optimization of the uncertainty set. The method of the present invention can effectively solve the problem of imbalance between system robustness and economy caused by output uncertainty under the condition of high proportion of renewable energy grid connection. The method solves the joint opportunity constraint equivalently from the perspective of robust optimization, and transforms the joint opportunity constraint into a robust optimization model that is easy to solve by constructing an uncertainty set that satisfies statistical feasibility; and uses a secondary optimization method based on the constraint uncertainty set to reduce the occurrence of wind and solar power curtailment.

Description

Technical Field

[0001] The present invention relates to renewable energy scheduling technology, and in particular to a statistically feasible distributed renewable energy joint opportunity constraint scheduling method. Background Art

[0002] Currently, an increasing number of distributed renewable energy generation resources are being connected to distribution networks. Compared to traditional generators, distributed renewable energy sources, such as wind and solar power, are significantly more weather-dependent, and their output fluctuations exhibit multi-dimensional uncertainties in time and space. As renewable energy penetration continues to increase, inaccurate forecasts of renewable energy generation capacity can lead to problems such as insufficient system ramping capacity. Therefore, it is necessary to fully account for the uncertainty of renewable energy when scheduling renewable energy generation output.

[0003] To address these challenges, uncertainty optimization theory has been extensively studied in the field of power dispatch, primarily encompassing stochastic optimization, robust optimization, and chance-constrained programming. Stochastic optimization relies on a known uncertainty distribution function, representing uncertainty by generating a large number of scenarios or selecting typical scenarios. The optimization solution is then assigned weights to the objective function according to the probabilities of different scenarios. However, this approach suffers from two major issues: first, low computational efficiency, and second, the assumed uncertainty distribution may not match the actual situation. Robust optimization, on the other hand, does not require an assumption about the sample distribution. Instead, it constructs an uncertainty set using sample data, requiring that constraints be satisfied within this uncertainty set. While this approach ensures robustness and does not require an uncertainty distribution function, it is overly conservative, leading to significant wind and solar curtailment.

[0004] Chance constraints allow the system to violate the constraint with a low probability, thus achieving a balance between robustness and optimality. However, due to the implicit nature of chance constraints, they must be converted into other forms before they can be solved. There are three main approaches to handling chance constraints: scenario-based methods, sampling average approximation methods, and analytical formula-based methods. Scenario-based methods rely on extensive data sampling and require that a certain number of samples satisfy the constraint conditions. However, this approach suffers from low computational efficiency, and the economic efficiency of the optimized scheduling results decreases as the number of samples increases. The sampling average approximation method introduces binary variables to approximate the probability of a sample violating the constraint. This method is somewhat less conservative than the scenario-based method, but still requires a large number of samples. The introduction of binary variables transforms the model into a mixed integer programming problem. Its computational efficiency also decreases significantly when the model conditions are complex. Analytical formula-based methods assume that the distribution function of the uncertainty variable is known, such as the standard normal distribution or other common types. By solving the cumulative probability distribution function, the quantile corresponding to the allowable risk value can be obtained. However, in practical applications, the distribution function of the uncertainty variable often does not exactly conform to these specific distributions, limiting the feasibility of this method in practice.

[0005] Therefore, there is currently a lack of a method that can achieve uncertainty-optimized scheduling of distributed renewable energy without assuming that the prediction errors of distributed renewable energy satisfy a specific distribution function. In addition, stochastic optimization methods are based on preset probability distribution assumptions, constructing a set of multiple scenarios and weightedly calculating the expected objective function to optimize decisions. The limitations of this method are reflected in two aspects: first, the large-scale scenario generation leads to a sharp increase in computing load; second, there is a significant deviation between actual operating data and the assumed probability model. Furthermore, robust optimization defines an uncertainty set driven by sample data and forces all constraints to hold within the set range. Although this ensures system robustness, it leads to a large amount of wind and solar power curtailment due to overly conservative strategies. In addition, although chance-constrained programming has advantages in balancing risks and benefits, existing solution technologies are still limited by distribution assumptions and cannot effectively deal with non-parametric uncertainty problems.

[0006] It should be noted that the information disclosed in the above background technology section is only used to understand the background of this application, and therefore may include information that does not constitute prior art known to ordinary technicians in this field. Summary of the Invention

[0007] The main purpose of the present invention is to overcome the defects in the above-mentioned background technology and provide a statistically feasible distributed renewable energy joint opportunity-constrained scheduling method.

[0008] To achieve the above object, the present invention adopts the following technical solutions:

[0009] A statistically feasible distributed renewable energy joint opportunity-constrained scheduling method includes the following steps:

[0010] S1. Establish a joint opportunity-constrained scheduling model for distributed renewable energy: Considering the prediction error between the actual and predicted output of distributed renewable energy, as well as the error correlation between different renewable energy units, a joint opportunity-constrained model is constructed to ensure that the system meets the overall robustness requirements under the allowable probability of insufficient output;

[0011] S2. Build a statistically feasible optimization scheduling model: Based on historical sample data, transform the joint opportunity constraints into a robust optimization model. By selecting an uncertainty set that satisfies statistical feasibility, the opportunity constraints are equivalently converted into a robust optimization form to achieve the solvability of the model.

[0012] S3. Constructing an uncertainty set: Divide the historical sample data into two parts, one for estimating the shape and the other for estimating the radius parameter of the ellipse. The shape of the ellipse is determined by the statistical characteristics of the sample, and the radius parameter of the ellipse is estimated by scalarizing the sample data to construct an ellipse uncertainty set that meets statistical feasibility.

[0013] S4. Secondary optimization of the uncertainty set: Based on the initial scheduling solution and constraints, the uncertainty set is reconstructed to make it more consistent with the actual distribution, and the reconstructed uncertainty set is used to optimize the scheduling model, thereby reducing wind and solar power curtailment and improving the economic efficiency of scheduling.

[0014] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the statistically feasible distributed renewable energy joint opportunity-constrained scheduling method.

[0015] A computer program product includes a computer program, which implements the statistically feasible distributed renewable energy joint opportunity-constrained scheduling method when executed by a processor.

[0016] The present invention has the following beneficial effects:

[0017] The present invention proposes a statistically feasible method for joint opportunity-constrained scheduling of distributed renewable energy. By equivalently solving the joint opportunity constraints from the perspective of robust optimization, it solves the problems of wind and solar power curtailment in the prior art caused by inaccurate distribution assumptions, low computational efficiency, and excessive model conservatism. The present invention does not need to rely on specific distribution assumptions for uncertainty variables. It constructs an elliptical uncertainty set that meets statistical feasibility based on historical sample data, converts the joint opportunity constraints into a robust optimization model that can be analytically solved, and dynamically reconstructs the uncertainty set through secondary optimization to fit the actual distribution, effectively balancing the robustness and economy of the system. This method avoids the problems of large-scale scenario generation and distribution deviation of random optimization, overcomes the excessive conservatism of traditional robust optimization, and breaks through the dependence of opportunity-constrained programming on distribution assumptions, significantly improving the computational efficiency and adaptability of the scheduling model, reducing the impact of renewable energy output forecast errors on the system's ramping ability, and ultimately achieving the coordinated optimization of the economy and reliability of power scheduling under a high proportion of renewable energy grid connection.

[0018] Other beneficial effects of the embodiments of the present invention will be further described below. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 Flowchart of a statistically feasible distributed renewable energy joint opportunity-constrained scheduling method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0020] The following is a detailed description of the embodiments of the present invention. It should be emphasized that the following description is only exemplary and is not intended to limit the scope of the present invention and its application.

[0021] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of such features. In the description of the embodiments of the present invention, "plurality" means two or more, unless otherwise specifically defined.

[0022] See Figure 1 The embodiment of the present invention provides a statistically feasible distributed renewable energy joint opportunity-constrained scheduling method, comprising the following steps:

[0023] Step S1, establishing a joint opportunity constraint scheduling model for distributed renewable energy: considering the prediction error between the actual output and the predicted output of distributed renewable energy, as well as the error correlation between different renewable energy units, a joint opportunity constraint model is constructed to ensure that the system meets the overall robustness requirements under the allowed probability of insufficient output.

[0024] In a preferred embodiment, step S1 specifically includes: defining the prediction error relationship between the actual output and the predicted output of distributed renewable energy, taking into account the prediction error correlation caused by the physical proximity of wind power generation and photovoltaic power generation units in the distribution network; constructing a joint opportunity constraint model based on the prediction error and its correlation, and the model ensures the overall robustness of the system through probabilistic constraints, and its constraint condition is to allow the probability of insufficient wind and solar output not to exceed a preset threshold; converting the joint opportunity constraint model into a unified standard mathematical form, in which the decision variables, uncertainty variables and constant coefficient matrices together constitute a linear constraint relationship to adapt to the subsequent transformation and solution of the robust optimization model.

[0025] Step S2: Construct a statistically feasible optimization scheduling model: Based on historical sample data, transform the joint opportunity constraints into a robust optimization model. By selecting an uncertainty set that satisfies statistical feasibility, the opportunity constraints are equivalently transformed into a robust optimization form to achieve the solvability of the model.

[0026] In a preferred embodiment, step S2 specifically includes: based on a set of historical sample data, verifying that the joint opportunity constraint meets the preset statistical confidence requirements to ensure the statistical feasibility of the scheduling model; according to the statistical confidence requirements, selecting an uncertainty set covering a preset probability threshold so that the uncertainty set can characterize the feasible range of the joint opportunity constraint; converting the joint opportunity constraint into a robust optimization constraint form, and constructing an analytically solvable robust optimization model by maximizing the influence of uncertainty variables on the constraint conditions.

[0027] Step S3: Constructing an uncertainty set: Divide the historical sample data into two parts, one for estimating the shape and radius parameters of the ellipse, respectively. The shape of the ellipse is determined by the sample statistical characteristics, and the radius parameters of the ellipse are estimated using the scalarized sorting of the sample data, thereby constructing an ellipse uncertainty set that meets statistical feasibility. The sample statistical characteristics may include the sample statistical mean and covariance matrix.

[0028] In a preferred embodiment, step S3 specifically includes: dividing the historical sample data set into a first subset and a second subset, the first subset is used to estimate the shape parameters of the elliptical uncertainty set, and the second subset is used to calculate the radius parameters of the elliptical uncertainty set; based on the sample data of the first subset, the center position of the elliptical uncertainty set is determined by statistical mean calculation, and the statistical distribution characteristics of the elliptical shape are determined by covariance analysis; each sample of the second subset is scalarized to generate a scalar sequence reflecting the degree of sample deviation, and the scalar sequence is arranged in ascending order to construct a statistical distribution boundary; according to a preset confidence threshold and risk tolerance, the radius parameter of the elliptical uncertainty set is dynamically determined based on the sorting result of the scalar sequence, so as to construct an elliptical uncertainty set that meets statistical feasibility; the constraint conditions of the elliptical uncertainty set are converted into a linear form, which is suitable for the analytical solution of the robust optimization model.

[0029] Step S4, secondary optimization of the uncertainty set: Based on the initial scheduling solution and constraints, the uncertainty set is reconstructed to make it more consistent with the actual distribution, so as to optimize the scheduling model using the reconstructed uncertainty set, thereby reducing wind and solar power curtailment and improving the economic efficiency of scheduling.

[0030] In a preferred embodiment, step S4 specifically includes: based on the initial scheduling solution and constraints, reconstructing the uncertainty set to compensate for the deviation between the ellipsoid set and the actual distribution, and the reconstructed set dynamically adapts to the feasible domain of the initial solution by introducing an adjustment coefficient; performing a multi-constraint joint scalar conversion on each sample of the second subset to generate a scalar sequence reflecting the degree of deviation of the sample from the initial solution, and constructing a statistical boundary by arranging them in ascending order; according to a preset confidence threshold and risk tolerance, dynamically determining the adjustment coefficient of the reconstructed set based on the sorting result of the scalar sequence to ensure that the reconstructed set covers the uncertainty range under a preset probability; converting the constraints of the reconstructed uncertainty set into a linear dual form, and forming an analyzable optimization model with less wind and solar power curtailment by introducing non-negative weight variables to couple multiple constraint relationships; using the linear dual model to update the scheduling decision, iteratively optimize the adjustment coefficient and weight variable, and achieve a balanced improvement in scheduling economy and robustness.

[0031] The method of the present invention can realize the uncertainty optimization scheduling of distributed renewable energy without assuming that the renewable energy prediction error satisfies a specific distribution function, while achieving a balance between robustness and economy.

[0032] The following further describes specific embodiments of the present invention and algorithm examples.

[0033] A statistically feasible method for joint opportunity-constrained scheduling of distributed renewable energy resources. Figure 1This method solves the joint opportunity constraints equivalently from the perspective of robust optimization. By constructing an uncertainty set that satisfies statistical feasibility, the joint opportunity constraints are transformed into a robust optimization model that is easy to solve. The quadratic optimization method based on the constraint uncertainty set is used to reduce the occurrence of wind and solar power curtailment.

[0034] Specifically, the following steps may be included:

[0035] (1) Establishing a joint opportunity-constrained dispatch model for distributed renewable energy

[0036] In order to take into account the forecast errors of the available power from wind and PV power generation, as well as the correlation between these errors due to the physical proximity of wind and PV units in the distribution network, the following joint chance constraint is adopted to ensure the overall robustness of the system:

[0037] (1)

[0038] Where, and Indicates the actual output of wind turbines and photovoltaics; and Indicates the predicted maximum available power of wind turbines and photovoltaics; and represents the prediction error; is the probability that the allowed wind and solar power output is insufficient.

[0039] The joint opportunity constraint shown in formula (1) can be expressed in the following unified standard form:

[0040] (2)

[0041] Where, represents the decision variable; represents an uncertainty variable; 、 and is a constant coefficient matrix; Indicates the number of constraints.

[0042] (2) Establish a statistically feasible optimization scheduling model

[0043] Based on historical sample data sets ,If the joint chance constraint (2) satisfies formula (3), then its statistical feasibility confidence is :

[0044] (3)

[0045] According to formula (3), by selecting the uncertainty set satisfy The joint chance constraint (2) can be transformed into the following form based on robust optimization:

[0046] (4)

[0047] (3) Construction of uncertainty set

[0048] It is necessary to use historical sample data sets Determine the elliptical uncertainty set that satisfies the conditions shown in formulas (5) and (6) :

[0049] (5)

[0050] (6)

[0051] Where M is a symmetric positive definite matrix; is the center of the elliptical uncertainty set; is the radius of the uncertainty ellipse.

[0052] To determine the elliptical uncertainty set Parameters, first divide the historical sample data set into and Two parts. To estimate the shape parameters and M, Used to calculate the radius parameter :

[0053] 1) Shape estimation: Defined as The sample mean in , Defined as The sample covariance matrix in :

[0054] (7)

[0055] (8)

[0056] 2) Radius correction: Each data sample in The function defined by formula (9) converted to scalars, and all of these Arranged in ascending order into a new set The critical value parameter is obtained by formula (10): , the required r Available Make an estimate:

[0057] (9)

[0058] (10)

[0059] when The sample size satisfies When , the uncertainty set obtained according to the above method meets the statistical feasibility requirements defined by formula (3). Based on the constructed ellipsoid uncertainty set, formula (4) can be equivalently transformed into the following linear form that is easy to solve:

[0060] (11)

[0061] (4) Secondary optimization of uncertainty set

[0062] Considering that there is still a deviation between the ellipsoid uncertainty set and the actual distribution function of the uncertainty variable, the feasible initial solution is obtained by optimizing the scheduling using formula (11). After that, the uncertainty set can be reconstructed into the following form based on the initial solution and constraints:

[0063] (12)

[0064] (13)

[0065] Where, Is to define the reconstruction uncertainty set Parameters; Represents the reconstruction uncertainty set.

[0066] The parameters are determined in a similar way to constructing the initial uncertainty set. . Each data sample in The function defined by formula (14) converted to scalars, and all of these Arranged in ascending order into a new set Required Available Make an estimate:

[0067] (14)

[0068] Where, and is a small positive number to prevent numerical problems, and Compare Several orders of magnitude larger.

[0069] Using the uncertainty set reconstructed based on the initial solution and constraints , Equation (4) can be equivalently transformed into the following linear form which is easy to solve and has less wind and solar power curtailment:

[0070] (15)

[0071] (16)

[0072] In the formula The uncertainty set for reconstruction The dual variable of .

[0073] An embodiment of the present invention further provides a storage medium for storing a computer program, which at least performs the above method when executed.

[0074] An embodiment of the present invention further provides a control device, comprising a processor and a storage medium for storing a computer program; wherein the processor is configured to execute at least the method described above when executing the computer program.

[0075] An embodiment of the present invention further provides a processor, which executes a computer program and at least performs the method described above.

[0076] The storage medium can be implemented by any type of non-volatile storage device, or a combination thereof. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a magnetic random access memory (FRAM), a flash memory, a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM); the magnetic surface memory can be a magnetic disk memory or a magnetic tape memory. The storage medium described in the embodiments of the present invention is intended to include, but is not limited to, these and any other suitable types of memory.

[0077] In the several embodiments provided by the present invention, it should be understood that the disclosed systems and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0078] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0079] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0080] Those skilled in the art will appreciate that all or part of the steps of the above-mentioned method embodiments may be implemented by hardware associated with program instructions, and the aforementioned program may be stored in a computer-readable storage medium. When the program is executed, the program executes the steps of the above-mentioned method embodiments. The aforementioned storage medium includes various media that can store program codes, such as mobile storage devices, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0081] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0082] The methods disclosed in the several method embodiments provided by the present invention can be arbitrarily combined without conflict to obtain new method embodiments.

[0083] The features disclosed in several product embodiments provided by the present invention can be arbitrarily combined without conflict to obtain new product embodiments.

[0084] The features disclosed in several method or device embodiments provided by the present invention can be arbitrarily combined without conflict to obtain new method embodiments or device embodiments.

[0085] The above is a further detailed description of the present invention in conjunction with specific preferred embodiments, and the specific implementation of the present invention should not be considered to be limited to these descriptions. Those skilled in the art will recognize that, without departing from the scope of the present invention, several equivalent substitutions or obvious variations can be made, and the performance or use of the same should be considered to fall within the scope of protection of the present invention.

Claims

1. A statistically feasible method for joint opportunity-constrained scheduling of distributed renewable energy resources, characterized in that: The following steps are involved: S1. Establish a joint opportunity-constrained scheduling model for distributed renewable energy: Considering the prediction error between the actual and predicted output of distributed renewable energy, as well as the error correlation between different renewable energy units, a joint opportunity-constrained scheduling model is constructed to ensure that the system meets the overall robustness requirements under the allowable probability of insufficient output; S2. Construct a statistically feasible robust optimization model: Based on historical sample data, the joint opportunity constraint scheduling model is transformed into a robust optimization model. By selecting an uncertainty set that satisfies statistical feasibility, the opportunity constraints are equivalently converted into a robust optimization form to achieve model solvability. S3. Constructing an uncertainty set: Divide the historical sample data into two parts, one for estimating the shape and the other for estimating the radius parameter of the ellipse. The shape of the ellipse is determined by the statistical characteristics of the sample, and the radius parameter of the ellipse is estimated by scalarizing the sample data to construct an ellipse uncertainty set that meets statistical feasibility. Converting the constraints of the elliptical uncertainty set into a linear form suitable for analytical solution of a robust optimization model; S4. Secondary optimization of the uncertainty set: Based on the initial scheduling solution and constraints, the uncertainty set is reconstructed to make it more consistent with the actual distribution, and the reconstructed uncertainty set is used to optimize the robust optimization model, thereby reducing wind and solar power curtailment and improving the economic efficiency of scheduling.

2. The method according to claim 1, characterized in that Step S1 specifically includes: Define the prediction error relationship between the actual output and the predicted output of distributed renewable energy, taking into account the prediction error correlation caused by the physical proximity of wind power generation and photovoltaic power generation units in the distribution network; Based on the prediction errors and their correlations, a joint opportunity-constrained scheduling model is constructed. The joint opportunity-constrained scheduling model ensures the overall robustness of the system through probabilistic constraints, and the constraint condition is that the probability of insufficient wind and solar power output is allowed to not exceed a preset threshold; The joint opportunity-constrained scheduling model is transformed into a unified standard mathematical form, in which the decision variables, uncertainty variables and constant coefficient matrices together constitute a linear constraint relationship to adapt to the subsequent transformation and solution of the robust optimization model.

3. The method according to claim 1, characterized in that Step S2 specifically includes: Based on a set of historical sample data, verify that the joint opportunity constraints meet the preset statistical confidence requirements to ensure the statistical feasibility of the scheduling model; According to the statistical confidence requirement, selecting an uncertainty set that covers a preset probability threshold, so that the uncertainty set can represent a feasible range of the joint opportunity constraint; The joint chance constraint is transformed into a robust optimization constraint form, and an analytically solvable robust optimization model is constructed by maximizing the influence of uncertainty variables on the constraint conditions.

4. The method according to claim 1, wherein In step S3, the sample statistical characteristics include the sample statistical mean and covariance matrix.

5. The method according to claim 4, characterized in that Step S3 specifically includes: Dividing the historical sample data set into a first subset and a second subset, the first subset is used to estimate the shape parameters of the ellipse uncertainty set, and the second subset is used to calculate the radius parameters of the ellipse uncertainty set; Based on the sample data of the first subset, determining the center position of the ellipse uncertainty set by statistical mean calculation, and determining the statistical distribution characteristics of the ellipse shape by covariance analysis; Performing a scalar conversion on each sample of the second subset to generate a scalar sequence reflecting the degree of sample deviation, and arranging the scalar sequence in ascending order to construct a statistical distribution boundary; According to a preset confidence threshold and risk tolerance, the radius parameter of the elliptical uncertainty set is dynamically determined based on the sorting result of the scalar sequence, so as to construct an elliptical uncertainty set that meets statistical feasibility.

6. The method according to claim 1, characterized in that Step S4 specifically includes: Based on the initial scheduling solution and the constraints, the uncertainty set is reconstructed to compensate for the deviation between the ellipsoid set and the actual distribution. The reconstructed uncertainty set dynamically adapts to the feasible domain of the initial solution by introducing an adjustment coefficient. Perform multi-constrained joint scalar transformation on each sample of the second subset to generate a scalar sequence reflecting the degree of deviation between the sample and the initial solution, and construct a statistical boundary by arranging them in ascending order; According to a preset confidence threshold and risk tolerance, an adjustment coefficient of the reconstruction set is dynamically determined based on the sorting result of the scalar sequence to ensure that the reconstruction set covers the uncertainty range under the preset probability; The constraints of the reconstructed uncertainty set are converted into a linear dual form, and a analyzable optimization model for reducing wind and solar power curtailment is formed by introducing non-negative weight variables to couple multiple constraints. The linear dual model is used to update the scheduling decision, and the adjustment coefficients and weight variables are iteratively optimized to achieve a balanced improvement in scheduling economy and robustness.

7. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the statistically feasible distributed renewable energy joint opportunity-constrained scheduling method according to any one of claims 1 to 6 is implemented.

8. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the statistically feasible distributed renewable energy joint opportunity-constrained scheduling method according to any one of claims 1 to 6 is implemented.

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