Statistically feasible distributed renewable energy joint opportunity constraint scheduling method, computer readable storage medium and program product

Abstract

The invention provides a statistically feasible distributed renewable energy joint opportunity constraint scheduling method, a computer readable storage medium and a program product. Comprising the steps of establishing a distributed renewable energy joint opportunity constraint scheduling model, constructing a statistically feasible optimal scheduling model, constructing an uncertainty set and performing secondary optimization on the uncertainty set. The method provided by the invention can effectively solve the problem of unbalance of system robustness and economy caused by output uncertainty under high-proportion renewable energy grid connection. The method comprises the following steps: equivalently solving a joint opportunity constraint from the perspective of robust optimization, and converting the joint opportunity constraint into a robust optimization model convenient to solve through uncertainty set construction meeting statistical feasibility; and a quadratic optimization method based on a constrained uncertainty set is utilized to reduce the generation of wind and light curtailment.

Description

Technical Field

[0001] The 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] At present, more and more distributed renewable energy generation resources are connected to the distribution network. Compared with traditional generators, distributed renewable energy represented by wind and solar resources has significant meteorological dependence characteristics, and its output fluctuations show multi-dimensional uncertainty characteristics in time and space. Against the background of increasing penetration of renewable energy, inaccurate prediction of renewable energy generation capacity will lead to problems such as insufficient system climbing ability. Therefore, it is necessary to fully consider the uncertainty of renewable energy when scheduling renewable energy generation output.

[0003] To meet the above challenges, uncertainty optimization theory has been widely studied in the field of power dispatching, mainly including stochastic optimization, robust optimization and chance constrained programming. Stochastic optimization relies on a known uncertainty distribution function, represents uncertainty by generating a large number of scenarios or selecting typical scenarios, and optimizes and solves the problem by allocating the weight of the objective function according to the probability of different scenarios. However, this method has two main problems: one is that the computational efficiency is low, and the other is that the assumed uncertainty distribution may not match the actual situation. Robust optimization does not require the distribution of the sample, but uses the sample data to construct an uncertainty set, requiring that the constraints must be met within the range of this uncertainty set. Although this method can ensure robustness and does not require an uncertainty distribution function, it is too conservative, resulting in a large amount of wind and solar power abandonment.

[0004] Chance constraints allow the system to violate constraints with a small probability, thereby achieving a balance between robustness and optimality. However, due to the implicit nature of chance constraints, they need to be converted into other forms before they can be solved. There are three main methods for dealing with chance constraints: scenario-based methods, sampling average approximation methods, and analytical formula-based methods. The scenario-based method relies on extensive sampling of data samples and requires a certain number of samples to satisfy the constraints. However, this method has the problem of low computational efficiency, and as the number of samples increases, the economy of the optimal scheduling results will gradually decrease. The sampling average approximation method introduces binary variables to approximate the probability that the sampled samples violate the constraints. Compared with the scenario-based method, this method reduces conservatism to a certain extent, but still requires a large number of samples, and due to the introduction of binary variables, the model becomes a mixed integer programming problem. When the model conditions are complex, its computational efficiency will also be significantly reduced. The analytical formula-based method assumes 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 allowed 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, so the feasibility of this method in practice is limited.

[0005] Therefore, there is currently a lack of a method that can achieve uncertainty optimization scheduling for distributed renewable energy without assuming that the prediction error of distributed renewable energy satisfies a specific distribution function. In addition, the stochastic optimization method is based on the preset probability distribution assumption, and optimizes the decision by constructing a multi-scenario set and weighted calculation of the expected objective function. 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 the 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 range of the set. Although it can ensure the robustness of the system, it leads to a large amount of wind and solar abandonment due to overly conservative strategies. In addition, although chance-constrained programming has advantages in balancing risks and benefits, the existing solution technology is still limited by the distribution assumption premise and it is difficult to 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 for understanding the background of the present application, and therefore may include information that does not constitute prior art known to ordinary technicians in the field. Summary of the invention

[0007] The main purpose of the present invention is to overcome the defects existing 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: A statistically feasible distributed renewable energy joint opportunity-constrained scheduling method includes the following steps: S1. Establish 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; 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 converted into a robust optimization form to achieve the solvability of the model. S3. Construct uncertainty set: divide the historical sample data into two parts, which are used to estimate the shape and radius parameters of the ellipse respectively. The shape of the ellipse is determined by the statistical characteristics of the sample, and the radius parameters of the ellipse are estimated by scalar sorting of the sample data, so as to construct an ellipse uncertainty set that meets statistical feasibility. S4. Secondary optimization of 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 use the reconstructed uncertainty set to optimize the scheduling model, thereby reducing wind and solar power abandonment and improving the economy of scheduling.

[0009] 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.

[0010] A computer program product comprises a computer program, wherein when the computer program is executed by a processor, the statistically feasible distributed renewable energy joint opportunity-constrained scheduling method is implemented.

[0011] The present invention has the following beneficial effects: 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, the problem of wind and solar power abandonment caused by inaccurate distribution assumptions, low computational efficiency, and excessive model conservatism in the prior art is solved. 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 improves the computational efficiency and adaptability of the scheduling model, reduces the impact of renewable energy output prediction errors on the system's ramping ability, and ultimately achieves the coordinated optimization of the economy and reliability of power scheduling under a high proportion of renewable energy grid connection.

[0012] Other beneficial effects of the embodiments of the present invention will be further described below. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 A flowchart of a statistically feasible distributed renewable energy joint opportunity-constrained scheduling method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0014] 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 and application of the present invention.

[0015] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0016] See also Figure 1 The embodiment of the present invention provides a statistically feasible distributed renewable energy joint opportunity constraint scheduling method, comprising the following steps: 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.

[0017] In a preferred embodiment, step S1 specifically includes: defining a 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, wherein the 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 not to exceed a preset threshold; converting the joint opportunity constraint model into a unified standard mathematical form, wherein 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.

[0018] Step S2, constructing a statistically feasible optimization scheduling model: based on historical sample data, transforming the joint opportunity constraints into a robust optimization model, and by selecting an uncertainty set that satisfies statistical feasibility, equivalently transforming the opportunity constraints into a robust optimization form, thus achieving the solvability of the model; 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 constraint form of robust optimization, and constructing an analytically solvable robust optimization model by maximizing the influence of uncertainty variables on the constraint conditions.

[0019] Step S3, constructing an uncertainty set: dividing the historical sample data into two parts, respectively used to estimate the shape and radius parameters of the ellipse, determining the shape of the ellipse by the sample statistical characteristics, and then estimating the radius parameters of the ellipse by scalarizing 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.

[0020] 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 constraints of the elliptical uncertainty set are converted into a linear form, which is suitable for the analytical solution of the robust optimization model.

[0021] 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 use the reconstructed uncertainty set to optimize the scheduling model, thereby reducing wind and solar power abandonment and improving the economy of scheduling.

[0022] 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 arranging them in ascending order to construct a statistical boundary; 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 abandonment 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.

[0023] 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, and at the same time achieve a balance between robustness and economy.

[0024] The specific embodiments of the present invention and algorithm examples thereof are further described below.

[0025] 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 secondary optimization method based on the constraint uncertainty set is used to reduce the occurrence of wind and solar power curtailment.

[0026] The specific steps may include: (1) Establishing a joint opportunity-constrained dispatch model for distributed renewable energy In order to take into account the forecast errors of the available power from wind and PV generation, as well as the correlation between these errors due to the physical proximity of wind and PV generation units in the distribution network, the following joint chance constraint is adopted to ensure the overall robustness of the system: (1) In the formula, and Indicates the actual output of wind turbines and photovoltaics; and represents 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. The joint opportunity constraint shown in formula (1) can be expressed in the following unified standard form: (2) In the formula, represents the decision variable; represents an uncertainty variable; , and is a constant coefficient matrix; Indicates the number of constraints.

[0027] (2) Establish a statistically feasible optimization scheduling model Based on historical sample data collection ,If the joint chance constraint (2) satisfies formula (3), then its statistical feasibility confidence is : (3) 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: (4) (3) Construction of uncertainty set Need to use historical sample data sets Determine the ellipse uncertainty set that satisfies the conditions shown in formulas (5) and (6): : (5) (6) Where M is a symmetric positive definite matrix; is the center of the ellipse uncertainty set; is the radius of the uncertainty ellipse.

[0028] To determine the ellipse 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 : 1) Shape estimation: Defined as The sample mean in , Defined as The sample covariance matrix in is: (7) (8) 2) Radius correction: Each data sample in The function defined by formula (9) converted to a scalar, and all of these are sorted in ascending order into a new set The critical value parameter is obtained by formula (10): , the required r Available Make an estimate: (9) (10) when The sample size satisfies When , the uncertainty set obtained by 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: (11) (4) Secondary Optimization of Uncertain Sets Considering that there is still a deviation between the ellipsoid uncertainty set and the actual distribution function of the uncertainty variable, a 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: (12) (13) In the formula, is the definition of the reconstruction uncertainty set Parameters; Represents the reconstruction uncertainty set.

[0029] 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 a scalar, and all of these are sorted in ascending order into a new set Required Available Make an estimate: (14) In the formula, and is a small positive number to prevent numerical problems, and Compare Several orders of magnitude larger.

[0030] 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 abandonment: (15) (16) In the formula The uncertainty set for reconstruction The dual variable of .

[0031] 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.

[0032] 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.

[0033] An embodiment of the present invention further provides a processor, wherein the processor executes a computer program and at least executes the method described above.

[0034] 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 ferromagnetic random access memory (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 disk memory or a 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.

[0035] 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 only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, 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.

[0036] 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 present embodiment.

[0037] 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.

[0038] A person skilled in the art can understand that: all or part of the steps of implementing the above method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiment; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), disks or optical disks, etc. Various media that can store program codes.

[0039] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function 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 can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product 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 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.

[0040] The methods disclosed in the several method embodiments provided by the present invention can be arbitrarily combined without conflict to obtain new method embodiments.

[0041] The features disclosed in several product embodiments provided by the present invention can be arbitrarily combined without conflict to obtain new product embodiments.

[0042] 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.

[0043] The above contents are further detailed descriptions of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art of the present invention, several equivalent substitutions or obvious variations can be made without departing from the concept of the present invention, and the performance or use is the same, which should be regarded as belonging to the protection scope of the present invention.

Claims

1. A statistically feasible method for joint opportunity-constrained scheduling of distributed renewable energy sources, characterized in that: The following steps are involved: S1. Establish 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; 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 converted into a robust optimization form to achieve the solvability of the model. S3. Construct uncertainty set: divide the historical sample data into two parts, which are used to estimate the shape and radius parameters of the ellipse respectively. The shape of the ellipse is determined by the statistical characteristics of the sample, and the radius parameters of the ellipse are estimated by scalar sorting of the sample data, so as to construct an ellipse uncertainty set that meets statistical feasibility. S4. Secondary optimization of 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 use the reconstructed uncertainty set to optimize the scheduling model, thereby reducing wind and solar power abandonment and improving the economy 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 error and its correlation, a joint chance constraint model is constructed, wherein the joint chance constraint model ensures the overall robustness of the system through probability constraints, and the constraint condition is that the probability of insufficient wind and solar power output is allowed not to exceed a preset threshold; The joint chance constraint 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 the historical sample data set, 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 covering a preset probability threshold, so that the uncertainty set can characterize a feasible range of the joint opportunity constraint; The joint chance constraint is transformed into a constraint form of robust optimization, 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, characterized in that: 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, wherein 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 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, a 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 constraints of the elliptic uncertainty set are converted into a linear form suitable for analytical solution of the robust optimization model.

6. The method according to claim 1, characterized in that Step S4 specifically includes: Based on the initial scheduling solution and the constraint conditions, the uncertainty set is reconstructed to compensate for the deviation between the ellipsoid set and the actual distribution, and 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 constraint conditions of the reconstructed uncertainty set are converted into a linear dual form, and a analyzable optimization model with less wind and solar power abandonment is formed by introducing non-negative weight variables to couple multiple constraint relationships; The linear dual model is used to update the scheduling decision, and the adjustment coefficient and weight variable 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 as described in 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 as described in any one of claims 1 to 6 is implemented.

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