Interregional power transmission data downscaling method and system based on step-by-step clustering analysis

Abstract

The invention belongs to the field of interregional power data analysis, and provides an interregional power transmission data downscaling method and system based on step-by-step clustering analysis, and the technical scheme is that the method comprises the steps: building a power call-in and call-out balance equation of a corresponding region based on the obtained power call-in and call-out data of a power grid of each region; the method comprises the following steps: clustering regions based on the actual communication condition of inter-region power transmission and transmission capacity data, distributing power transmission data corresponding to a clustering result to small region units, and in the distribution process, carrying out data estimation on the units with missing data, constructing a power balance constraint condition of the downscaled small region unit based on a power call-in and call-out balance equation of the region; and constructing an objective function based on the power transmission data obtained by each distribution unit, and optimizing the objective function to obtain an optimized scheme. And more reliable data support is provided for power grid planning, scheduling and optimization.

Description

Technical Field

[0001] The present invention belongs to the field of inter-regional power data analysis, and in particular relates to an inter-regional power transmission data downscaling method and system based on stepwise clustering analysis. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] With the continuous development and expansion of the power system, the accurate analysis and prediction of inter-regional power transmission data has become increasingly important. However, the existing power transmission data analysis methods have the following problems and shortcomings:

[0004] 1. Insufficient data granularity: Currently, most power transmission data analysis methods mainly process data at the provincial level or larger, making it difficult to accurately analyze and predict power transmission conditions in smaller areas. This results in a lack of sufficiently detailed data support when planning and optimizing regional power grids.

[0005] 2. Ignoring the actual connectivity between regions: Existing methods often ignore the actual power transmission capacity and connectivity between different regions, which may lead to unrealistic results when conducting power transmission analysis.

[0006] 3. High computational complexity: Some existing data downscaling methods have high computational complexity and are difficult to apply in real time in large-scale power grid systems, which affects the efficiency of power grid scheduling and optimization.

[0007] 4. Lack of comprehensive consideration of multi-dimensional data: Many existing methods only focus on single-dimensional data, such as power load or power generation, while ignoring other important factors such as carbon emissions, grid losses, etc., which limits the comprehensiveness and accuracy of the analysis results.

[0008] 5. Insufficient adaptability: The power system is a complex system that changes dynamically. Many existing methods lack sufficient adaptability and are unable to cope with changes in the grid topology and operating mode.

[0009] 6. Data consistency issues: When analyzing inter-regional power transmission data, we often face inconsistencies between data from different sources and at different scales, which increases the difficulty of data processing and analysis. Summary of the invention

[0010] In order to solve at least one technical problem existing in the above-mentioned background technology, the present invention provides an inter-regional power transmission data downscaling method and system based on stepwise clustering analysis, which takes into account the actual connectivity between regions and multi-dimensional data, allocates the clustering results to smaller regional units, and obtains an optimized solution based on the allocated data.

[0011] In order to achieve the above object, the present invention adopts the following technical solution:

[0012] A first aspect of the present invention provides an inter-regional power transmission data downscaling method based on stepwise cluster analysis, comprising the following steps:

[0013] Obtain the power transfer data of each regional power grid, the actual connectivity of inter-regional power transmission and the transmission capacity data;

[0014] Based on the acquired power inflow and outflow data of each regional power grid, a power inflow-out balance equation of the corresponding region is established;

[0015] Cluster regions based on the actual connectivity and transmission capacity data of inter-regional power transmission, and allocate the power transmission data corresponding to the clustering results to small regional units. During the allocation process, estimate the data for units with missing data, and construct the power balance constraint conditions of the small regional units after downscaling based on the regional power in-out balance equation.

[0016] Based on the power transmission data obtained from each distribution unit, an objective function is constructed, and the objective function is optimized to obtain an optimized solution.

[0017] Furthermore, the power transfer data of each regional power grid, the actual connectivity of inter-regional power transmission and the transmission capacity data are obtained;

[0018] Based on the acquired power inflow and outflow data of each regional power grid, a power inflow-out balance equation of the corresponding region is established;

[0019] Cluster regions based on the actual connectivity and transmission capacity data of inter-regional power transmission, and allocate the power transmission data corresponding to the clustering results to small regional units. During the allocation process, estimate the data for units with missing data, and construct the power balance constraint conditions of the small regional units after downscaling based on the regional power in-out balance equation.

[0020] Based on the power transmission data obtained from each distribution unit, an objective function is constructed, and the objective function is optimized to obtain an optimized solution.

[0021] Furthermore, Kriging interpolation is used to estimate the missing small area unit data.

[0022] Furthermore, the power balance constraint of the small area unit is:

[0023] ∑G k =G i ,∑L k =L i ,∑I k =I i ,∑Ok =O i ,

[0024] Among them, k represents the small area unit in area i, G k , L k ,I k , O k They represent the power generation, power consumption, power input and power output of unit k respectively, G i is the power generation of region i, I i To adjust the power, L i is the power consumption, O i To adjust the power.

[0025] Furthermore, the allocation process also includes minimizing the difference between the estimated value and the constraint condition by the least square method, so as to simultaneously satisfy the estimated result of the Kriging interpolation and the power balance constraint.

[0026] Furthermore, the objective function is:

[0027] min f=α*∑|ΔP k |+β*∑E k +γ*∑C k +δ∑Cost k ,

[0028] C k =∑(G kj *EF j ),

[0029] E k = r*(I k +O k )*D k ,

[0030] Cost k =P k *G k +T k *(I k +O k ),

[0031] Among them, C k is the carbon emission per unit, G kj is the electricity generation of energy type j in region k, EF j is the carbon emission factor of each energy source, r is the loss rate per unit distance, D k is the average transmission distance, P k is the unit power generation cost, T k is the unit transmission cost, ΔP k is the power imbalance of unit k, and α, β, γ are the corresponding weight coefficients.

[0032] Furthermore, the method also includes setting a data acquisition cycle, calculating a relative change rate based on data acquired in the latest cycle and data acquired in the previous cycle, and re-clustering if the relative change rate exceeds a set threshold.

[0033] A second aspect of the present invention provides an inter-regional power transmission data downscaling system based on stepwise cluster analysis, comprising:

[0034] A data acquisition module is used to obtain the power transfer data of each regional power grid, the actual connectivity of inter-regional power transmission and the transmission capacity data;

[0035] Based on the acquired power inflow and outflow data of each regional power grid, a power inflow-out balance equation of the corresponding region is established;

[0036] The downscaling module is used to cluster regions based on the actual connectivity and transmission capacity data of inter-regional power transmission, and allocate the power transmission data corresponding to the clustering results to small regional units. During the allocation process, the data of the units with missing data are estimated, and the power balance constraints of the small regional units after downscaling are constructed based on the regional power transfer-in-out balance equation;

[0037] The target optimization module is used to construct a target function based on the power transmission data obtained from each distribution unit, optimize the target function, and obtain an optimized solution.

[0038] A third aspect of the present invention provides a computer-readable storage medium.

[0039] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps in the inter-regional power transmission data downscaling method based on stepwise clustering analysis as described above.

[0040] A fourth aspect of the present invention provides a computer device.

[0041] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps in the inter-regional power transmission data downscaling method based on stepwise clustering analysis as described above are implemented.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] The present invention realizes efficient processing of large-scale power transmission data by introducing a stepwise clustering analysis method, thus solving the problem of high computational complexity of traditional methods; through a multi-dimensional data analysis framework, a comprehensive evaluation of power balance, transmission loss, carbon emissions and economic costs is realized, thus solving the limitation of single-dimensional analysis in the prior art; the present invention realizes more accurate data downscaling by considering the actual power transmission capacity and connectivity between regions, thus solving the problem of traditional methods ignoring the actual power grid topology.

[0044] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0046] Figure 1 It is a flow chart of a method for downscaling inter-regional power transmission data based on stepwise clustering analysis provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0047] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0048] It should be noted that the following detailed descriptions are all illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0049] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0050] Embodiment 1

[0051] This embodiment provides an inter-regional power transmission data downscaling method based on stepwise cluster analysis, comprising the following steps:

[0052] Step 1: Obtain the power inflow and outflow data of each regional power grid, the actual connectivity of inter-regional power transmission, and the transmission capacity data;

[0053] In this embodiment, the power transfer-in and transfer-out data of the power grids in each region include the power generation and consumption, the power transfer-in and the power transfer-out of each region.

[0054] The actual connectivity and transmission capacity data of inter-regional power transmission include the physical connection of transmission lines (such as the number of lines, voltage level, and line length), the rated transmission capacity of each line, and the actual available transmission capacity.

[0055] Step 2: Preprocess the acquired power inflow and outflow data of each regional power grid, the actual connectivity of inter-regional power transmission, and the transmission capacity data;

[0056] In this embodiment, the preprocessing process includes standardizing the data to eliminate the influence of different dimensions;

[0057] Step 3: Based on the acquired power inflow and outflow data of each regional power grid, establish a power inflow-out balance equation for the corresponding region;

[0058] In this embodiment, the power transfer-in-out balance equation is expressed as:

[0059] G i +I i =L i +O i ,

[0060] Among them, G i is the power generation of region i, I i To adjust the power, L i is the power consumption, O i To adjust the power.

[0061] Step 4: Treat each region as an independent cluster, cluster the regions based on the actual connectivity and transmission capacity data of inter-regional power transmission, and update the power inflow-outflow balance equation based on the clustering results;

[0062] The specific steps include:

[0063] Step 401, initialization: each regional power grid is regarded as an independent cluster;

[0064] Step 402, calculate the distance between clusters: based on the power transmission data and the actual connectivity, calculate the distance between clusters. The distance function is defined as follows:

[0065]

[0066] in, Δ P mn is the net power transfer between clusters m and n, T mn is the actual transmission capacity between m and n,Δ E mn is the energy structure difference between m and n, w1, w2, w3 are weight coefficients.

[0067] Step 403, cluster merging: merging clusters whose distance between clusters is less than a set threshold;

[0068] Step 404, updating the balance equation: updating the power in-out balance equation based on the newly obtained clustering result;

[0069] Step 405: Repeat steps 402 to 404 until a preset number of clusters is reached or a termination condition is met.

[0070] Step 5: Allocate the power transmission data corresponding to the clustering results to the small regional units. During the allocation process, estimate the data for the units with missing data, and construct the power balance constraint conditions of the small regional units after downscaling based on the regional power transfer-in-out balance equation.

[0071] The specific steps include:

[0072] Step 501: Combine the clustering results of step 4 and distribute the power transmission data in each area to each unit;

[0073] Step 502: determine whether the data of each unit is missing, and if so, estimate the data of the unit with missing data;

[0074] In this embodiment, Kriging interpolation is used to estimate the missing small area unit data, and the value of the unknown point is estimated based on the power transmission data points of the known points. The specific formula is:

[0075] Z s0=∑λ i Z i ,

[0076] Among them, Z s0 is the value of the point to be estimated, Z s i is the value of the known point, λ i is the weight coefficient, obtained by solving the Kriging equations.

[0077] Step 503: construct power balance constraint conditions of the downscaled unit data based on the regional power import-export balance equation;

[0078] To ensure that the downscaled data meets the power balance requirements, the following constraints are applied:

[0079] ∑G k =G i ,∑L k =L i ,∑I k =Ii ,∑O k =O i ,

[0080] Among them, k represents the small area unit in area i, G k , L k ,I k , O k They represent the power generation, power consumption, power input and power output of unit k respectively.

[0081] These constraints ensure that the downscaled small-area data are consistent with the original provincial data in total, maintaining the power balance.

[0082] Step 504: In practical applications, multiple iterations are required to adjust the unit data to simultaneously satisfy the estimation result of the Kriging interpolation and the power balance constraint.

[0083] In this embodiment, the difference between the estimated value and the constraint condition is minimized by the least squares method or other optimization algorithms to simultaneously satisfy the estimation result of the Kriging interpolation and the power balance constraint, thereby obtaining a unit power transmission data estimation result that conforms to statistical principles and satisfies physical constraints.

[0084] Step 6: Based on the power transmission data obtained from each distribution unit, construct an objective function, optimize the objective function, and obtain the optimized inter-regional power transmission data downscaling result;

[0085] Specifically include:

[0086] Step 601, calculating the carbon emissions, transmission losses and electricity production and transmission costs of each unit;

[0087] Among them, the carbon emission factors of different energy types are introduced to calculate the carbon emissions of each region. The calculation formula of carbon emissions is:

[0088] C k =∑(G kj *EF j ),

[0089] In the formula, C k is the carbon emission per unit, G kj is the electricity generation of energy type j in region k, EF j is the carbon emission factor of each energy source (unit: t CO2 / MWh);

[0090] Among them, considering the transmission distance and loss rate, the energy loss of different transmission paths is calculated, and the calculation formula of transmission loss is:

[0091] E k = r*(I k +Ok )*D k ,

[0092] In the formula, r is the loss rate per unit distance, D k is the average transmission distance;

[0093] Considering the cost of electricity production and transmission, the economic benefits of different transmission schemes are evaluated. The calculation formula for the cost of electricity production and transmission is:

[0094] Cost k =P k *G k +T k *(I k +O k ),

[0095] Among them, P k is the unit power generation cost, T k is the unit transmission cost;

[0096] Step 602: Construct an objective function by combining the power imbalance, the carbon emissions of each unit, and the transmission loss, which is expressed as:

[0097] minf=α*∑|ΔP k |+β*∑E k +γ*∑C k +δ∑Cost k ,

[0098] Among them, ΔP k is the power imbalance of unit k, α, β, γ, δ are the corresponding weight coefficients;

[0099] Step 603: Optimize the constructed objective function to obtain an optimized inter-regional power transmission data downscaling result;

[0100] In this embodiment, a genetic algorithm is used to optimize the downscaling result, and the specific steps are as follows:

[0101] Step 6031, initializing the population: generating an initial solution set according to the downscaling results.

[0102] Step 6032, fitness evaluation: calculate the objective function value of each solution as the fitness.

[0103] Step 6033, selection: Use roulette wheel selection method to select outstanding individuals.

[0104] Step 6034, crossover: Generate new solutions using uniform crossover method.

[0105] Step 6035, mutation: randomly perturb the solution with a certain probability.

[0106] Step 6036, elite retention: retain the best solution in each generation.

[0107] Step 6037, iteration: repeat steps 6032-6036 until the termination condition is reached.

[0108] By adjusting the weight coefficient of the objective function, power grid planning and optimization can be carried out according to different optimization goals, providing comprehensive data support and helping to make more scientific and reasonable decisions.

[0109] The optimized results can be used to more accurately assess the regional power supply and demand balance, identify high-loss transmission paths, provide a basis for grid planning, and then analyze carbon emissions in different regions to support the formulation of emission reduction strategies. Through this optimization process, downscaling results with improvements in power balance, transmission efficiency, and environmental impact were obtained.

[0110] Step 7: Monitor the power transmission data in real time, calculate the relative change rate based on the data obtained in the latest cycle and the data obtained in the previous cycle, and if the relative change rate exceeds the set threshold, return to step 4 to re-cluster and perform downscaling based on the new clustering results.

[0111] In this embodiment, the acquisition cycle is set as a monthly update cycle, and new provincial power transmission data is collected at the beginning of each month.

[0112] For example: collect data for December on January 1, collect data for January on February 1, and so on.

[0113] In this embodiment, the calculation formula of the relative change rate is:

[0114] ΔR = (new data - old data) / old data * 100%,

[0115] The threshold τ is set to 10%. When |ΔR|>τ, it is considered a significant change.

[0116] Through this dynamic adjustment mechanism, changes in the power system can be captured in a timely manner, ensuring the timeliness and accuracy of the downscaling results.

[0117] Embodiment 2

[0118] This embodiment provides an inter-regional power transmission data downscaling system based on stepwise cluster analysis, comprising:

[0119] A data acquisition module is used to obtain the power transfer data of each regional power grid, the actual connectivity of inter-regional power transmission and the transmission capacity data;

[0120] Based on the acquired power inflow and outflow data of each regional power grid, a power inflow-out balance equation of the corresponding region is established;

[0121] The downscaling module is used to cluster regions based on the actual connectivity and transmission capacity data of inter-regional power transmission, and allocate the power transmission data corresponding to the clustering results to small regional units. During the allocation process, the data of the units with missing data are estimated, and the power balance constraints of the small regional units after downscaling are constructed based on the regional power transfer-in-out balance equation;

[0122] The target optimization module is used to construct a target function based on the power transmission data obtained from each distribution unit, optimize the target function, and obtain an optimized solution.

[0123] Embodiment 3

[0124] This embodiment provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the steps in the inter-regional power transmission data downscaling method based on stepwise clustering analysis as described above are implemented.

[0125] Embodiment 4

[0126] This embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps in the inter-regional power transmission data downscaling method based on stepwise clustering analysis as described above are implemented.

[0127] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of hardware embodiments, software embodiments, or embodiments combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer-usable program code.

[0128] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0129] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0130] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0131] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0132] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for downscaling inter-regional power transmission data based on stepwise cluster analysis, characterized in that: The steps include: Obtain the power transfer data of each regional power grid, the actual connectivity of inter-regional power transmission and the transmission capacity data; Based on the acquired power inflow and outflow data of each regional power grid, a power inflow-out balance equation of the corresponding region is established; Cluster regions based on the actual connectivity and transmission capacity data of inter-regional power transmission, and allocate the power transmission data corresponding to the clustering results to small regional units. During the allocation process, estimate the data for units with missing data, and construct the power balance constraint conditions of the small regional units after downscaling based on the regional power in-out balance equation. Based on the power transmission data obtained from each distribution unit, an objective function is constructed, and the objective function is optimized to obtain an optimized solution.

2. The inter-regional power transmission data downscaling method based on stepwise cluster analysis according to claim 1, characterized in that: Clustering of regions based on actual inter-regional power transmission connectivity and transmission capacity data, including: Each regional power grid is considered as an independent cluster; Based on the power transmission data and actual connectivity, the distance between clusters is calculated; Clusters whose distance between clusters is less than the set threshold are merged.

3. The inter-regional power transmission data downscaling method based on stepwise cluster analysis according to claim 1, characterized in that: Use kriging interpolation to estimate missing small area unit data.

4. The inter-regional power transmission data downscaling method based on stepwise cluster analysis according to claim 1, characterized in that: The power balance constraints of small area units are: ΣG k =G i ,ΣL k =L i ,ΣI k =I i ,∑O k =O i , Among them, k represents the small area unit in area i, G k , L k ,I k , O k They represent the power generation, power consumption, power input and power output of unit k respectively, G i is the power generation of region i, I i To adjust the power, L i is the power consumption, O i To adjust the power.

5. The inter-regional power transmission data downscaling method based on stepwise cluster analysis according to claim 1, characterized in that: The allocation process also includes minimizing the difference between the estimated value and the constraint conditions through the least squares method to simultaneously meet the estimated results of Kriging interpolation and the power balance constraints.

6. The inter-regional power transmission data downscaling method based on stepwise cluster analysis according to claim 1, characterized in that: The objective function is: min f=α*∑|ΔP k |+β*∑E k +γ*ΣC k +δ∑Cost k , C k =∑(G kj *EF j ), E k =r*(I k +O k )*D k , Cost k =P k *G k +T k *(I k +O k ), Among them, C k is the carbon emission per unit, G kj is the electricity generation of energy type j in region k, EF j is the carbon emission factor of each energy source, r is the loss rate per unit distance, D k is the average transmission distance, P k is the unit power generation cost, T k is the unit transmission cost, ΔP k is the power imbalance of unit k, and α, β, γ, and δ are the corresponding weight coefficients.

7. The inter-regional power transmission data downscaling method based on stepwise cluster analysis according to claim 1, characterized in that: The method further includes setting a data acquisition cycle, calculating a relative change rate based on data acquired in a latest cycle and data acquired in a previous cycle, and re-clustering if the relative change rate exceeds a set threshold.

8. An inter-regional power transmission data downscaling system based on stepwise cluster analysis, characterized in that: include: A data acquisition module is used to obtain the power transfer data of each regional power grid, the actual connectivity of inter-regional power transmission and the transmission capacity data; Based on the acquired power inflow and outflow data of each regional power grid, a power inflow-out balance equation of the corresponding region is established; The downscaling module is used to cluster regions based on the actual connectivity and transmission capacity data of inter-regional power transmission, and allocate the power transmission data corresponding to the clustering results to small regional units. During the allocation process, the data of the units with missing data are estimated, and the power balance constraints of the small regional units after downscaling are constructed based on the regional power transfer-in-out balance equation; The target optimization module is used to construct a target function based on the power transmission data obtained from each distribution unit, optimize the target function, and obtain an optimized solution.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps in the inter-regional power transmission data downscaling method based on stepwise clustering analysis as described in any one of claims 1 to 7 are implemented.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps in the inter-regional power transmission data downscaling method based on stepwise clustering analysis as described in any one of claims 1 to 7 are implemented.

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