Multi-stage limit switching optimization method for uncertainty of new energy scene

Abstract

The invention relates to the technical field of power systems, and particularly discloses a new energy scene uncertainty-oriented multi-stage limit switching optimization method, which comprises the following steps of: constructing a distribution robust optimization model considering new energy extreme scene uncertainty and section limit constraint; a multi-section dynamic limit constraint set is obtained based on a SimpleMKKM model; obtaining an operation mode cluster set # imgabs0 # with a classification label, wherein the operation mode cluster set # imgabs0 # comprises a plurality of sub-operation mode cluster sets # imgabs1 #, and each sub-cluster set reflects one type of typical operation mode; and multi-stage limit switching optimization is carried out based on an operation mode and a clustering center least two norm as a criterion. The method has the advantages that multi-stage flexible switching can be carried out on diversified operation scene sections, and new energy consumption is promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a multi-level quota switching optimization method for uncertainties in new energy scenarios. Background Art

[0002] With the deepening reform of the global energy structure, the access of a high proportion of renewable energy to the power system has become the core path to promote energy transformation. However, the inherent intermittency and randomness of renewable energy generation significantly increase the uncertainty of power system operation, posing severe challenges to the assessment of transmission network section limits. As an important indicator to measure the anti-disturbance capability and stability of the power grid, the section limit is the key to ensuring the safe operation of the system, especially for the weak points of the hub power grid. However, traditional section assessment methods mostly rely on the total transfer capacity (TTC) of typical operating modes, and rely on historical operating conditions, which makes it difficult to cope with the complex nonlinear characteristics and scenario diversification problems brought about by the increase in the penetration rate of new energy. In addition, the setting of TTC is a computationally intensive task with high computational requirements. Its computational burden hinders the accurate search for safe operation boundaries based on TTC assessment safety margin methods.

[0003] Artificial intelligence algorithms have been widely studied and applied to complex calculations in power systems due to their powerful nonlinear fitting capabilities and high computational efficiency. Existing artificial intelligence algorithms (deep neural networks, reinforcement learning, etc.) have demonstrated outstanding computing performance in TTC evaluation and control, but the "common problem" of deep learning - poor interpretability - is still a key barrier to its implementation in the industry. There are two key issues that need to be addressed in accurate and highly reliable section limit assessment and robust optimization scheduling: First, the uncertainty characterization of new energy scenarios needs to take into account the random occurrence of extreme scenarios, and the probability distribution of new energy uncertainty should satisfy a certain degree of uncertainty, that is, the new energy scenarios used for learning can support robust and generalizable learning; second, the section limit assessment calculation should adapt to a variety of operating scenarios, that is, there should be high requirements for the accuracy of the limit, but at the same time, the computational efficiency and engineering practicality should be guaranteed. Summary of the invention

[0004] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a multi-level limit switching optimization method for new energy scenario uncertainty.

[0005] The purpose of the present invention is achieved through the following technical solution: a multi-level quota switching optimization method for new energy scene uncertainty, comprising the following steps:

[0006] Step S1, constructing a distributed robust optimization model considering the uncertainty of new energy extreme scenarios and section limit constraints;

[0007] The distributed robust optimization model is expressed as:

[0008]

[0009] in, and Represent the scheduling time zone, section set, scenario set and probability distribution set respectively, p sc represents the occurrence probability of scenario sc; H(·) and G(·) are the equality constraint and inequality constraint in the model respectively; are the unit adjustment price coefficients in the day-ahead unit combination model and the new energy multi-scenario; P G (t), P D (t), P RE (t) and u G (t) are respectively the active output of synchronous generator, active power of load, active power of new energy grid-connected, and start / stop status of synchronous generator; They are positive deviation, negative deviation, positive deviation state and negative deviation state of the active output of the synchronous generator in scenario sc respectively; is the predicted value of new energy under scenario sc; is the power flow transfer factor matrix; P f (t),P f,sc (t) are the transmission power of section f in the initial prediction scenario and the uncertainty scenario sc respectively; A is the parameter matrix of feature adjustment in the new energy scenario, reflecting the changes of the day-ahead dispatch results in each scenario; f(·) is a multi-level limit switcher based on the distance criterion; is the multi-section dynamic limit constraint set; ξ sc is the random prediction error of new energy;

[0010] Step S2: Based on the SimpleMKKM model, a multi-section dynamic limit constraint set is obtained; and an operation mode cluster with classification labels is obtained. It contains multiple sub-operation mode clusters Each sub-cluster reflects a typical operation mode;

[0011] Step S3: switching the multi-level quota based on the operation mode and the minimum square norm of the cluster center as the criterion.

[0012] Specifically, the random prediction error ξ sc and the probability of occurrence of scenario sc p sc The following mathematical relationship is satisfied:

[0013]

[0014] Where N is the total number of samples; interval The uncertainty range of the mean probability distribution of new energy scenarios is defined. Specifically, the distributed robust optimization model is reconstructed into a main problem and A two-stage model of the subproblems is solved by means of sequence and generation constraints.

[0015] Specifically, the multi-core K-means clustering model is expressed as:

[0016]

[0017] In the formula, and are the basic kernel weight set and cluster partition matrix set respectively, is a set of sampled operation modes, including all historical operations and their searched extreme operation scenarios, x i is the q-dimensional operation mode feature vector obtained after feature extraction, K γ is a set of precomputed kernel matrices; represents the pth kernel function, m and k are the number of kernel functions and clusters respectively, γ p and H represent the weight of the pth basic kernel and the weight of the clustering partition matrix respectively.

[0018] Specifically, the multi-core K-means clustering model is reconstructed as a minimization problem, specifically:

[0019]

[0020] Specifically, a simplified gradient descent algorithm is used to iteratively update γ:

[0021]

[0022] Specifically, in step S3, the multi-level limit switching is performed by the following formula:

[0023]

[0024] Among them, j is the category index.

[0025] The present invention has the following advantages:

[0026] The present invention sets interval constraints on the mean value of new energy distribution, constructs a distributionally robust optimization model that considers new energy extreme scenarios and section limit constraints, improves the robustness of the section limit switcher in a high-proportion new energy system, applies a multi-core clustering algorithm based on multi-core spatial mapping to identify diverse operating modes, and constructs multi-level section limit rules, thereby achieving robust switching of section multi-level limits. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a schematic diagram of the flow chart of the switching optimization method of the present invention. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention, that is, the embodiments described are only part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations.

[0029] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of the present invention.

[0030] It should be noted that relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "including a..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0031] The present invention is further described below in conjunction with the accompanying drawings, but the protection scope of the present invention is not limited to the following description. Figure 1 As shown in FIG. 1 , the multi-level quota switching optimization method for new energy scenario uncertainty includes the following steps:

[0032] Step S1, construct a distributionally robust optimization model that considers the uncertainty of extreme scenarios of new energy and the constraints of cross-sectional limits; in order to enhance the robustness of the model, the present invention incorporates the uncertainty of renewable energy prediction and establishes a scenario-oriented distributionally robust optimization model (SDRO), which integrates multiple random scenarios of new energy. By effectively avoiding complex mathematical transformations, the model has better engineering practicality; the distributionally robust optimization model is expressed as:

[0033]

[0034] in, and Represent the scheduling time zone, section set, scenario set and probability distribution set respectively, p sc represents the occurrence probability of scenario sc; H(·) and G(·) are the equality constraint and inequality constraint in the model respectively; are the unit adjustment price coefficients in the day-ahead unit combination model and the new energy multi-scenario; P G (t), P D (t), P RE (t) and u G (t) are respectively the active output of synchronous generator, active power of load, active power of new energy grid-connected, and start / stop status of synchronous generator; They are positive deviation, negative deviation, positive deviation state and negative deviation state of the active output of the synchronous generator in scenario sc respectively; is the predicted value of new energy under scenario sc; P f,sc (t) is the transmission power of section f in scenario sc; A is the parameter matrix of feature adjustment in the new energy scenario, reflecting the changes of day-ahead dispatch results in each scenario; f(·) is a multi-level limit switcher based on distance criterion; is the multi-section dynamic limit constraint set; ξ sc is the random prediction error of new energy;

[0035] The random prediction error ξ sc and the probability p of scenario sc sc The following mathematical relationship is satisfied:

[0036]

[0037] The probability distribution of the new energy scenario is constrained by the Wasserstein radius, which limits its deviation from the probability distribution of the sampled operating mode dataset. The uncertainty range of the mean probability distribution of new energy scenarios is defined; by adjusting μ, the robustness of the model in extreme scenarios can be enhanced. (1) and (2) A robust unit commitment model is constructed based on dynamic section limit switching. The model can be reconstructed into a main problem and A two-stage model for each subproblem, which can be solved by column-and-constraint generation (C&CG);

[0038] Step S2: Based on the SimpleMKKM model, a multi-section dynamic limit constraint set is obtained; and an operation mode cluster with classification labels is obtained. It contains multiple sub-operation mode clusters Each sub-cluster reflects a typical operation mode;

[0039] Traditional section limits only consider a single conservative limit value. The reason is that the setting of the limit only considers the single feature of the section flow and the stability verification result. The insufficient characterization of the operating mode leads to the generation of a "one-size-fits-all" conservative limit. Considering a wider range of operating characteristics can characterize more accurate limit boundaries, but after introducing more features, there is a linear inseparability problem between operating modes, especially in large-scale power systems. In order to solve this problem, the present invention adopts multi-core unsupervised learning, such as multi-core K-means clustering (Multiple Kernel K-means Clustering, MKKM), to project the operating mode into the multi-core Hilbert space, which enhances the ability to capture the complex relationship between the modes and can identify different operating modes. Specifically, a global optimal algorithm SimpleMKKM is adopted, which has been proven to have excellent generalization error performance and is used to generate representative operating modes;

[0040] The SimpleMKKM model is expressed as:

[0041]

[0042] In the formula, and are the basic kernel weight set and cluster partition matrix set respectively, is a set of sampled operation modes, including all historical operations and their searched extreme operation scenarios, x i is the q-dimensional operation mode feature vector obtained after feature extraction, K γ is a set of precomputed kernel matrices; represents the pth kernel function, m and k are the number of kernel functions and clusters respectively, γ p and H represent the weight of the pth basic kernel and the weight of the clustering partition matrix respectively.

[0043] Specifically, the multi-core K-means clustering model is reconstructed as a minimization problem, where It is proved to be differentiable, specifically:

[0044]

[0045] A simplified gradient descent algorithm is used to iteratively update γ to ensure the global accelerated convergence of the model:

[0046]

[0047] Through (6), we obtain the decreasing direction of γ as d = [d1,…,d m ] Τ .

[0048] The SimpleMKKM model is optimized by the following algorithm:

[0049] 1: Input: t=1,flag=1.

[0050] 2:while flag do:

[0051] 3: By solving (4b) Calculate H

[0052] 4: Calculate d p (p=1,…,m).

[0053] 5: Update γ (t+1) =γ (t) +αd

[0054] 6:if max|γ (t+1) -γ (t) |<1e-5then

[0055] 7: flag = 0.

[0056] 8:end if

[0057] 9:t=t+1

[0058] 10: end while

[0059] 11:

[0060] 12:Output:y

[0061] Through the above-mentioned pattern recognition algorithm based on SimpleMKKM, the operation mode clusters with classification labels are obtained Contains multiple sub-runtime clusters The modes in each sub-operation mode cluster have the highest degree of feature similarity, and each sub-cluster can reflect a typical operation mode, where Through this method, the present invention alleviates the strong nonlinear separability of the safety boundary caused by characterizing the operation mode only by section power transmission, and uses a conservative setting method in each sub-cluster set:

[0062] Select the maximum cross-sectional flow in the stable mode and the minimum cross-sectional flow in the unstable mode within the cluster, and take the minimum of the two;

[0063] Step S3: Using the operation mode and the minimum square norm of the cluster center as the criterion to switch the multi-level quota:

[0064]

[0065] Among them, j is the category index.

[0066] The above is only a preferred embodiment of the present invention and does not limit the present invention in any form. Any technician familiar with the art can make many possible changes and modifications to the technical solution of the present invention by using the above-mentioned technical content without departing from the scope of the technical solution of the present invention, or modify it into an equivalent embodiment of equivalent changes. Therefore, any changes, modifications, equivalent changes and modifications made to the above embodiments based on the technology of the present invention without departing from the content of the technical solution of the present invention belong to the protection scope of the present technical solution.

Claims

1. A multi-level quota switching optimization method for new energy scenario uncertainty, characterized by: The following steps are involved: Step S1, constructing a distributed robust optimization model considering the uncertainty of new energy extreme scenarios and section limit constraints; The distributed robust optimization model is expressed as: in, and Represent the scheduling time zone, section set, scenario set and probability distribution set respectively, p sc represents the occurrence probability of scenario sc; H(·) and G(·) are the equality constraint and inequality constraint in the model respectively; are the unit adjustment price coefficients in the day-ahead unit combination model and the new energy multi-scenario; P G (t), P D (t), P RE (t) and u G (t) are respectively the active output of synchronous generator, active power of load, active power of new energy grid-connected, and start / stop status of synchronous generator; They are positive deviation, negative deviation, positive deviation state and negative deviation state of the active output of the synchronous generator in scenario sc respectively; is the predicted value of new energy under scenario sc; is the power flow transfer factor matrix; P f (t), P f，sc (t) are the transmission power of section f in the initial prediction scenario and the uncertainty scenario sc respectively; A is the parameter matrix of feature adjustment in the new energy scenario, reflecting the changes of the day-ahead dispatch results in each scenario; f(·) is a multi-level limit switcher based on the distance criterion; is the multi-section dynamic limit constraint set; ξ sc is the random prediction error of new energy; Step S2: Based on the SimpleMKKM model, a multi-section dynamic limit constraint set is obtained; and an operation mode cluster with classification labels is obtained. It contains multiple sub-operation mode clusters Each sub-cluster reflects a typical operation mode; Step S3: switching the multi-level quota based on the operation mode and the minimum square norm of the cluster center as the criterion.

2. The multi-level quota switching optimization method for new energy scenario uncertainty according to claim 1 is characterized by: The random prediction error ξ sc and the probability p of scenario sc sc The following mathematical relationship is satisfied: Where N is the total number of samples; interval The uncertainty range of the mean probability distribution of new energy scenarios is defined.

3. The multi-level quota switching optimization method for new energy scenario uncertainty according to claim 2 is characterized by: The distributed robust optimization model is reformulated as a master problem and A two-stage model of the subproblems is solved by means of sequence and generation constraints.

4. The multi-level quota switching optimization method for new energy scenario uncertainty according to claim 1 is characterized by: The SimpleMKKM model in step S2 is expressed as: In the formula, is the field of real numbers, and They are the basic kernel weight set and the cluster partition matrix set, E is the sampling operation mode set, including all historical operations and their search extreme operation scenarios, x i is the q-dimensional operation mode feature vector obtained after feature extraction, K γ is a set of pre-computed kernel matrices, the kernel matrix K calculated by multiple independent kernel functions P Composition, γ is the weight coefficient vector of each kernel matrix; represents the pth kernel function, m and k are the number of kernel functions and clusters respectively, γ p and H represent the weight of the pth basic kernel and the weight of the clustering partition matrix respectively.

5. The multi-level quota switching optimization method for new energy scenario uncertainty according to claim 4 is characterized in that: The multi-core K-means clustering model is restructured as a minimization problem, specifically:

6. The multi-level quota switching optimization method for new energy scenario uncertainty according to claim 5 is characterized by: Use a simplified gradient descent algorithm to iteratively update γ:

7. The multi-level quota switching optimization method for new energy scenario uncertainty according to claim 6 is characterized by: In step S3, multi-level quota switching is performed by the following formula: Among them, j is the category index.

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