A wind power carrying capacity analysis method and system based on inverse peak regulation characteristics

Abstract

The application discloses a wind power bearing capacity analysis method and system based on anti-peaking characteristics, and the method comprises the following steps: performing cluster analysis on the normalized daily wind power output characteristic data set to obtain a clustering result; based on the clustering result, calculating a wind power clustering weighted anti-peaking coefficient and a wind power clustering typical characteristic anti-peaking coefficient; according to the information of the installed capacity of each type in the to-be-tested region, calculating the total amount of peak regulation supply in the to-be-tested region; taking the total amount of peak regulation supply greater than or equal to the total demand for peak regulation as a constraint, and based on the wind power clustering weighted anti-peaking coefficient and the wind power clustering typical characteristic anti-peaking coefficient, respectively calculating a wind power clustering weighted bearing capacity and a wind power clustering typical characteristic bearing capacity; and based on the wind power clustering weighted bearing capacity and the wind power clustering typical characteristic bearing capacity, obtaining the wind power bearing capacity interval of the to-be-tested region. The application can accurately solve the range interval of the wind power bearing capacity of the region on the basis of calculating the peak regulation supply capacity of each type of power supply in the region.

Description

Technical Field

[0001] This invention relates to a method and system for analyzing wind power carrying capacity based on anti-peak shaving characteristics, belonging to the field of power system planning. Background Technology

[0002] New energy sources such as wind power and solar power have become an important part of my country's power system. However, while the large-scale grid connection of new energy sources brings clean electricity, their inherent intermittency, randomness, and volatility also pose significant challenges to the real-time balance of the power system. Among these, the "anti-peak-shaving" characteristic of wind power is particularly prominent, meaning that during peak electricity demand periods, wind power output may be low, while during off-peak demand periods, wind power generation may be high. This characteristic greatly exacerbates the net load fluctuation of the power grid, rapidly depleting the peak-shaving capacity provided by conventional power sources within the system, leading to increasingly prominent difficulties in balancing the power system and security issues.

[0003] Currently, existing methods for analyzing wind power carrying capacity are overly simplistic: when assessing regional wind power carrying capacity, many methods fail to fully consider the coordinated regulation capabilities of multiple power sources (such as coal-fired power, gas-fired power, energy storage, and hydropower) within the region. Traditional deterministic methods or simple proportional allocation methods are insufficient to accurately depict the complex dynamic balance between system peak-shaving demand and peak-shaving supply under high wind power penetration.

[0004] In conclusion, how to quantitatively analyze the wind power anti-peak-shaving characteristics based on actual wind power output data, and scientifically analyze the regional carrying capacity in conjunction with the different types of installed capacity in the region, is a problem worthy of further research. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide a wind power carrying capacity analysis method and system based on anti-peak shaving characteristics. This method can accurately solve the range of wind power carrying capacity in a region by calculating the peak shaving supply capacity of various types of power sources in the region and taking the system's peak shaving supply capacity meeting the peak shaving demand as the objective.

[0006] To achieve the above objectives, the present invention is implemented using the following technical solution:

[0007] In a first aspect, the present invention provides a method for analyzing wind power carrying capacity based on anti-peak shaving characteristics, including:

[0008] Obtain the historical wind power operation dataset for the area to be tested;

[0009] The historical wind power operation dataset is normalized to obtain a normalized daily wind power output characteristic dataset;

[0010] Cluster analysis is performed on the normalized daily wind power output characteristic dataset to obtain cluster results. The cluster results include cluster centers, as well as the normalized daily wind power output characteristic data and their quantity represented by each cluster center.

[0011] Based on the clustering results, calculate the wind power cluster weighted anti-peak-shaving coefficient and the wind power cluster typical characteristic anti-peak-shaving coefficient;

[0012] Based on the installed capacity information of various types in the area to be tested, calculate the total peak-shaving supply in the area to be tested;

[0013] With the constraint that the total peak-shaving supply is greater than or equal to the total peak-shaving demand, the wind power cluster weighted carrying capacity and the wind power cluster typical characteristic carrying capacity are calculated based on the wind power cluster weighted anti-peak-shaving coefficient and the wind power cluster typical characteristic anti-peak-shaving coefficient, respectively.

[0014] The wind power carrying capacity range of the area to be tested is obtained based on the weighted carrying capacity of wind power clustering and the carrying capacity of typical characteristics of wind power clustering.

[0015] Optionally, obtaining the historical wind power operation dataset of the area to be tested includes:

[0016] Within a preset historical time period, the actual wind power output of the area to be tested is sampled at fixed frequency intervals to obtain the actual wind power output at different time points.

[0017] By combining the actual wind power output at different time points on a daily basis, multiple days of wind power output characteristic data are obtained.

[0018] Historical wind power operation datasets were obtained based on multiple daily wind power output characteristic data.

[0019] Optionally, the formula for the per-unit processing is expressed as follows:

[0020] ;

[0021] In the formula: This represents the wind power output characteristic data for the m-th day after standardization; This represents the wind power output characteristic data for day m. This indicates the installed capacity of the wind turbines in the area to be tested in the wind farm.

[0022] Optionally, cluster analysis of the standardized daily wind power output characteristic dataset includes:

[0023] S01: Preset multiple cluster centers;

[0024] S02: Calculate the Euclidean distance between all the normalized daily wind power output characteristic data and each cluster center in the normalized daily wind power output characteristic dataset;

[0025] S03: Based on the Euclidean distance, classify all the normalized daily wind power output characteristic data to obtain the classification results; the classification results include: the normalized daily wind power output characteristic data and their quantity represented by each cluster center;

[0026] S04: Update each cluster center according to the classification results to obtain multiple updated cluster centers;

[0027] S05: Calculate the similarity between each cluster center and its corresponding updated cluster center;

[0028] S06: If the similarity between each cluster center and the corresponding updated cluster center is not greater than the preset threshold, then the updated cluster center replaces the cluster center, and S02-S06 are repeated until the similarity is greater than the preset threshold.

[0029] If the similarity between each cluster center and its corresponding updated cluster center is greater than a preset threshold, then the updated cluster center replaces the original cluster center, and steps S02-S03 are executed to obtain the updated classification result. The updated classification result and the updated cluster center are then used as the clustering result.

[0030] Optionally, the calculation formula for the wind power clustering weighted anti-peak-shaving coefficient is expressed as follows:

[0031] ;

[0032] ;

[0033] In the formula, Represents the weighted anti-peak-shaving coefficient for wind power clustering; i represents the sequence number; The anti-peak-shaving coefficient represents the wind power characteristics of the i-th cluster center; This represents the number of normalized daily wind power output characteristic data represented by the i-th cluster center; This represents the total amount of daily wind power output characteristic data in the normalized daily wind power output characteristic dataset; This represents the maximum output coefficient of the wind power characteristics of the i-th cluster center; The minimum output coefficient of the wind power characteristic of the i-th cluster center is represented; the maximum output coefficient of the wind power characteristic of the i-th cluster center is the maximum value in the wind power output curve corresponding to the i-th cluster center, and the minimum output coefficient of the wind power characteristic of the i-th cluster center is the minimum value in the wind power output curve corresponding to the i-th cluster center.

[0034] Optionally, the formula for calculating the anti-peak-shaving coefficient of the typical characteristics of wind power clustering is expressed as follows:

[0035] ;

[0036] In the formula, Indicates the anti-peak-shaving coefficient, a typical characteristic of wind power clustering; The maximum output coefficient representing the wind power characteristics of a typical cluster center; The minimum output coefficient represents the wind power characteristics of a typical cluster center; the typical cluster center is the cluster center with the largest number of normalized daily wind power output characteristic data; the maximum output coefficient of the wind power characteristics of a typical cluster center is the maximum value in the wind power output curve corresponding to the typical cluster center, and the minimum output coefficient of the wind power characteristics of a typical cluster center is the minimum value in the wind power output curve corresponding to the typical cluster center.

[0037] Optionally, calculating the total peak-shaving supply in the area under test based on the installed capacity information of each type in the area under test includes:

[0038] Obtain the installed capacity information of the area to be tested, including the installed capacity type of the units and the installed capacity of different types of units;

[0039] Based on the installed capacity information of the area to be tested, calculate the maximum and minimum power output coefficients of units of different installed capacity types;

[0040] Based on the maximum and minimum power output coefficients of different installed capacity types, as well as the installed capacity scale information, calculate the total peak-shaving supply in the area to be tested.

[0041] The formula for calculating the total peak-shaving supply is as follows:

[0042] ;

[0043] ;

[0044] ;

[0045] In the formula, Indicates the total supply for peak shaving; This represents the total maximum output of all installed types; This represents the total minimum output of all installed types; j represents the sequence number; This represents the installed capacity of the unit of type j; This represents the maximum technical output coefficient of the unit of the j-th type of installed capacity. It represents the minimum technical output coefficient of the unit of the j-th type of installed capacity.

[0046] Optionally, the step of using the constraint that the total peak-shaving supply is greater than or equal to the total peak-shaving demand, and calculating the wind power cluster weighted carrying capacity and the wind power cluster typical characteristic carrying capacity based on the wind power cluster weighted anti-peak-shaving coefficient and the wind power cluster typical characteristic anti-peak-shaving coefficient respectively, includes:

[0047] Establish a model for calculating total peak-shaving demand;

[0048] Based on the aforementioned peak-shaving total demand calculation model and the constraint that the total peak-shaving supply is greater than or equal to the total peak-shaving demand, the wind power cluster weighted carrying capacity and the wind power cluster typical characteristic carrying capacity are solved.

[0049] The calculation model for total peak-shaving demand is expressed as follows:

[0050] ;

[0051] ;

[0052] in, Indicates the total demand for peak shaving; Indicates the wind power reverse peak shaving capacity; This indicates the typical characteristics and carrying capacity of wind power clusters; This represents the weighted carrying capacity of wind power clusters. This represents the wind power anti-peak shaving coefficient, including the wind power cluster weighted anti-peak shaving coefficient or the wind power cluster typical characteristic anti-peak shaving coefficient;

[0053] The formulas for solving the weighted carrying capacity of wind power clusters and the carrying capacity of typical characteristics of wind power clusters are expressed as follows:

[0054] ;

[0055] .

[0056] Optionally, the wind power carrying capacity range of the area to be tested is represented as: [ , ].

[0057] in, This represents the maximum value of the typical characteristics carrying capacity of wind power clusters; This represents the maximum value of the weighted carrying capacity of wind power clustering.

[0058] Secondly, the present invention provides a wind power carrying capacity analysis system based on anti-peak shaving characteristics, comprising:

[0059] The data acquisition module is used to acquire historical wind power operation datasets for the area under test.

[0060] The per-unit processing module is used to perform per-unit processing on the historical wind power operation dataset to obtain a per-unitized daily wind power output characteristic dataset.

[0061] The clustering analysis module is used to perform clustering analysis on the normalized daily wind power output characteristic dataset to obtain clustering results. The clustering results include cluster centers, as well as the normalized daily wind power output characteristic data and their quantity represented by each cluster center.

[0062] The anti-peak-shaving coefficient module is used to calculate the wind power cluster weighted anti-peak-shaving coefficient and the wind power cluster typical characteristic anti-peak-shaving coefficient based on the clustering results.

[0063] The total supply calculation module is used to calculate the total peak-shaving supply in the area under test based on the installed capacity information of various types in the area under test.

[0064] The carrying capacity calculation module is used to calculate the wind power cluster weighted carrying capacity and the wind power cluster typical characteristic carrying capacity based on the constraint that the total peak-shaving supply is greater than or equal to the total peak-shaving demand, and the wind power cluster weighted anti-peak-shaving coefficient and the wind power cluster typical characteristic anti-peak-shaving coefficient, respectively.

[0065] The interval confirmation module is used to obtain the wind power carrying capacity interval of the area to be tested based on the weighted carrying capacity of wind power clustering and the carrying capacity of typical characteristics of wind power clustering.

[0066] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0067] This invention proposes a method and system for analyzing wind power carrying capacity based on anti-peak shaving characteristics. The method performs cluster analysis on a standardized daily wind power output characteristic dataset to obtain clustering results. These results include cluster centers and the standardized daily wind power output characteristic data and their quantity represented by each cluster center. Based on the clustering results, two anti-peak shaving coefficients (wind power cluster weighted anti-peak shaving coefficient and wind power cluster typical characteristic anti-peak shaving coefficient) are calculated. With the goal of meeting peak shaving capacity demand with peak shaving capacity supply, the wind power carrying capacity range is calculated by combining the two anti-peak shaving coefficients. This range represents the recommended scale for reasonable wind power development within the region, providing a scale reference for future reasonable wind power construction in the region. The calculation method is simple and fast, enabling rapid analysis of wind power anti-peak shaving characteristics and reasonable development scale. It can provide a reference for regional wind power, new energy, and power planning, and has practical application value in promoting high-quality renewable energy consumption. Attached Figure Description

[0068] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly described below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort, wherein:

[0069] Figure 1 The flowchart shown is a method for analyzing wind power carrying capacity based on anti-peak shaving characteristics in an embodiment of the present invention. Detailed Implementation

[0070] The technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit this disclosure or its application or use. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without creative effort are within the scope of protection of this disclosure.

[0071] Unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of this disclosure.

[0072] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and equipment should be considered part of the specification.

[0073] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values.

[0074] The application principle of the present invention will be described in detail below with reference to the accompanying drawings.

[0075] Example 1

[0076] like Figure 1 As shown in the figure, this embodiment of the invention provides a method for analyzing wind power carrying capacity based on anti-peak shaving characteristics, including the following steps:

[0077] S1: Obtain the historical wind power operation dataset for the area to be tested;

[0078] S2: Standardize the historical wind power operation dataset to obtain a standardized daily wind power output characteristic dataset;

[0079] S3: Perform cluster analysis on the normalized daily wind power output characteristic dataset to obtain cluster results. The cluster results include cluster centers, as well as the normalized daily wind power output characteristic data and their quantity represented by each cluster center.

[0080] S4: Based on the clustering results, calculate the wind power cluster weighted anti-peak-shaving coefficient and the wind power cluster typical characteristic anti-peak-shaving coefficient;

[0081] S5: Calculate the total peak-shaving supply in the area under test based on the installed capacity information of each type in the area under test;

[0082] S6: With the constraint that the total peak-shaving supply is greater than or equal to the total peak-shaving demand, the wind power cluster weighted carrying capacity and the wind power cluster typical characteristic carrying capacity are calculated based on the wind power cluster weighted anti-peak-shaving coefficient and the wind power cluster typical characteristic anti-peak-shaving coefficient, respectively.

[0083] S7: The wind power carrying capacity range of the area to be tested is obtained based on the weighted carrying capacity of wind power clustering and the carrying capacity of typical characteristics of wind power clustering.

[0084] In this embodiment, the specific steps for obtaining the historical wind power operation dataset of the area to be tested in step S1 include:

[0085] S11: Within the preset historical time period for sampling, the actual wind power output of the area to be tested is sampled at fixed frequency intervals to obtain the actual wind power output at different time points.

[0086] Specifically, sampling can be performed at different frequency intervals based on different types of wind power in the area to be tested, such as offshore wind power and onshore wind power.

[0087] S12: Combine the actual wind power output at different time points on a daily basis to obtain multiple days of wind power output characteristic data;

[0088] S13: Obtain historical wind power operation dataset based on multiple daily wind power output characteristic data.

[0089] In this embodiment, the specific process of standardizing the historical wind power operation dataset in step S2 is as follows:

[0090] Standardize all daily wind power output characteristic data in the historical wind power operation dataset to obtain a standardized daily wind power output characteristic dataset.

[0091] The per-unit normalization formula is expressed as follows:

[0092] ;

[0093] In the formula: This represents the wind power output characteristic data for the m-th day after standardization; This represents the wind power output characteristic data for day m. This indicates the installed capacity of the wind turbines in the area to be tested in the wind farm.

[0094] The per-unit normalized daily wind power output characteristic dataset is represented as follows:

[0095]

[0096] In the formula: This represents the per-unit-level daily wind power output characteristics dataset; This represents the wind power output characteristics data for the first day after standardization; This represents the wind power output characteristics data for the second day after standardization; This represents the wind power output characteristics data for the third day after standardization.

[0097] In this embodiment, step S3 involves performing cluster analysis on the standardized daily wind power output characteristic dataset to obtain clustering results. These results include the standardized daily wind power output characteristic data and their quantity corresponding to each cluster center, including:

[0098] S01: Preset multiple cluster centers;

[0099] Specifically, in order to distinguish seasonal wind power output characteristics, the number of cluster centers k is generally greater than or equal to 4. In this embodiment, the number of cluster centers k=4.

[0100] S02: Calculate the Euclidean distance between all the normalized daily wind power output characteristic data and each cluster center in the normalized daily wind power output characteristic dataset;

[0101] S03: Based on the Euclidean distance, classify all the normalized daily wind power output characteristic data to obtain the classification results; the classification results include: the normalized daily wind power output characteristic data and their quantity represented by each cluster center;

[0102] Specifically, the cluster center with the smallest Euclidean distance calculated in step S02 is assigned to the category represented by that cluster center. After traversing all the data, this clustering is completed.

[0103] S04: Update each cluster center according to the classification results to obtain multiple updated cluster centers;

[0104] S05: Calculate the similarity between each cluster center and its corresponding updated cluster center;

[0105] S06: If the similarity between each cluster center and the corresponding updated cluster center is not greater than the preset threshold, then the updated cluster center replaces the cluster center, and S02-S06 are repeated until the similarity is greater than the preset threshold.

[0106] If the similarity between each cluster center and its corresponding updated cluster center is greater than a preset threshold, then the updated cluster center replaces the original cluster center, and steps S02-S03 are executed to obtain the updated classification result. The updated classification result and the updated cluster center are then used as the clustering result.

[0107] Specifically, steps S05 and S06 are significant because they calculate the similarity between the cluster centers before and after the update. If they are similar, the clusters are considered to have converged, and the final clustering result is obtained. If they are not similar, the clusters are considered to have not converged, and the cluster centers are updated again until convergence is achieved.

[0108] In this embodiment, K-means clustering analysis is used.

[0109] In this embodiment, step S4, based on the clustering results, calculates the wind power cluster weighted anti-peak-shaving coefficient and the wind power cluster typical characteristic anti-peak-shaving coefficient, including:

[0110] The calculation of the wind power cluster-weighted anti-peak-shaving coefficient is based on the number of normalized daily wind power output characteristic data, di, represented by each cluster center in the typical wind power output scenario cluster (cluster center type cluster):

[0111] ;

[0112] ;

[0113] In the formula, Represents the weighted anti-peak-shaving coefficient for wind power clustering; i represents the sequence number; The anti-peak-shaving coefficient represents the wind power characteristics of the i-th cluster center; This represents the number of normalized daily wind power output characteristic data represented by the i-th cluster center; This represents the total amount of daily wind power output characteristic data in the normalized daily wind power output characteristic dataset, in this embodiment. ; This represents the maximum output coefficient of the wind power characteristics of the i-th cluster center; The minimum output coefficient of the wind power characteristics of the i-th cluster center is represented by the maximum output coefficient of the wind power characteristics of the i-th cluster center, which is the maximum value in the wind power output curve corresponding to the i-th cluster center, and the minimum output coefficient of the wind power characteristics of the i-th cluster center is the minimum value in the wind power output curve corresponding to the i-th cluster center.

[0114] The calculation of the typical anti-peak-shaving coefficient of wind power clustering is based on the wind power characteristic with the largest peak-to-valley difference in the cluster with the largest number of clusters, thus reflecting the anti-peak-shaving demand of wind power. The formula for calculating the typical anti-peak-shaving coefficient of wind power clustering is as follows:

[0115] ;

[0116] In the formula, Indicates the anti-peak-shaving coefficient, a typical characteristic of wind power clustering; The maximum output coefficient representing the wind power characteristics of a typical cluster center; The minimum output coefficient represents the wind power characteristics of a typical cluster center; the typical cluster center is the cluster center with the largest number of normalized daily wind power output characteristic data; the maximum output coefficient of the wind power characteristics of a typical cluster center is the maximum value in the wind power output curve corresponding to the typical cluster center, and the minimum output coefficient of the wind power characteristics of a typical cluster center is the minimum value in the wind power output curve corresponding to the typical cluster center.

[0117] In this embodiment, step S5 calculates the total peak-shaving supply in the area under test based on the installed capacity information of each type in the area under test, including:

[0118] S51: Obtain the installed capacity information of the area to be tested, including the installed capacity type of the unit and the installed capacity of different types of units;

[0119] Specifically, the types of installed capacity may include: coal-fired power, heating gas turbines, peak-shaving gas turbines, nuclear power, energy storage, biomass power generation, hydropower, etc.

[0120] S52: Based on the installed capacity information of the area to be tested, calculate the maximum and minimum output coefficients of units of different installed capacity types;

[0121] S53: Calculate the total peak-shaving supply in the area to be tested based on the maximum and minimum output coefficients of different types of installed capacity, as well as the installed capacity information.

[0122] Specifically, the formula for calculating the total peak-shaving supply is as follows:

[0123] ;

[0124] ;

[0125] ;

[0126] In the formula, Indicates the total supply for peak shaving; This represents the total maximum output of all installed types; This represents the total minimum output of all installed types; j represents the sequence number; This represents the installed capacity of the unit of type j; This represents the maximum technical output coefficient of the unit of the j-th type of installed capacity. It represents the minimum technical output coefficient of the unit of the j-th type of installed capacity.

[0127] In this embodiment, step S6 takes the constraint that the total peak-shaving supply is greater than or equal to the total peak-shaving demand, and calculates the wind power cluster weighted carrying capacity and the wind power cluster typical characteristic carrying capacity based on the wind power cluster weighted anti-peak-shaving coefficient and the wind power cluster typical characteristic anti-peak-shaving coefficient, respectively, including:

[0128] S61: Establish a calculation model for total peak-shaving demand;

[0129] Specifically, the total demand calculation model for peak shaving is expressed as follows:

[0130] ;

[0131] ;

[0132] in, Indicates the total demand for peak shaving; Indicates the wind power reverse peak shaving capacity; This indicates the typical characteristics and carrying capacity of wind power clusters; This represents the weighted carrying capacity of wind power clusters. This represents the wind power anti-peak shaving coefficient, including the wind power cluster weighted anti-peak shaving coefficient or the wind power cluster typical characteristic anti-peak shaving coefficient;

[0133] S62: Based on the above peak-shaving total demand calculation model and the constraint that the total peak-shaving supply is greater than or equal to the total peak-shaving demand, solve the wind power cluster weighted carrying capacity and the wind power cluster typical characteristic carrying capacity;

[0134] The formulas for solving the weighted carrying capacity of wind power clusters and the carrying capacity of typical characteristics of wind power clusters are expressed as follows:

[0135] ;

[0136] ;

[0137] Combining the wind power cluster weighted anti-peak-shaving coefficient and the wind power cluster typical characteristic anti-peak-shaving coefficient calculated in step S4, the above formula can be transformed into:

[0138] ;

[0139] ;

[0140] In this embodiment, the wind power carrying capacity range of the area to be tested in step S7 is represented as: [ , ].

[0141] in, This represents the maximum value of the typical characteristics carrying capacity of wind power clusters; This represents the maximum value of the weighted carrying capacity of wind power clustering.

[0142] Specifically, in the wind power clustering weighted anti-peak-shaving coefficient Typical characteristics of wind power clustering and anti-peak regulation coefficient In the former case, because it simultaneously considers both the large and small requirements for anti-peak modulation characteristics, therefore... Compare Small. Therefore, the corresponding > .

[0143] Example 2

[0144] Based on the wind power carrying capacity analysis method based on anti-peak shaving characteristics proposed in Example 1, this example analyzes the wind power carrying capacity of offshore wind farms in a designated area, specifically including:

[0145] S01: Preprocess the offshore wind power data in this area, sample the offshore wind power data at a frequency of 1 hour, and standardize the output data at the same time. Finally, obtain the processed daily offshore wind power output characteristic set and the annual wind power output characteristic curve set and data dimension of 365×24.

[0146] S02: Set the number of clusters to 5, and perform K-means cluster analysis on the processed daily wind power output characteristics at sea.

[0147] S03: Obtain cluster characteristic clusters T i and the corresponding quantity d i The number of clusters in the five categories are 66, 47, 46, 48, and 158, respectively, with the peak-to-valley difference of the cluster centers for each category being 0.26, 0.22, 0.56, 0.54, and 0.06, respectively. The characteristics of the cluster centers and the largest peak-to-valley difference for each cluster are as follows:

[0148] T1={0.37,0.37,0.37,0.40,0.37,0.40,0.35,0.32,0.29,0.26,0.23,0. 21,0.21,0.19,0.17,0.17,0.17,0.18,0.21,0.16,0.14,0.20,0.19,0.2 1; 0.85, 0.71, 0.54, 0.44, 0.18, 0.21, 0.30, 0.29, 0.17, 0.10, 0.01, 0.00, 0.00, 0.00, 0.00, 0.04, 0.06, 0.04, 0.04, 0.07, 0.00, 0.97, 0.37, 0.02

[0149] T2={0.74,0.71,0.75,0.78,0.80,0.80,0.79,0.81,0.81,0.77,0.78,0. 83,0.79,0.79,0.82,0.86,0.89,0.94,0.93,0.93,0.92,0.88,0.83,0.8 0; 0.97, 0.12, 0.01, 0.24, 0.94, 0.95, 0.94, 0.75, 0.95, 0.95, 0.94, 0.94, 0.95, 0.95, 0.95, 0.95, 0.94, 0.94, 0.93, 0.94, 0.94, 0.93, 0.77, 0.72

[0150] T3 = {0.19, 0.18, 0.17, 0.19, 0.18, 0.20, 0.18, 0.19, 0.23, 0.23, 0.25, 0.30, 0.33, 0.38, 0.41, 0.49, 0.52, 0.57, 0.59, 0.67, 0.68, 0.72, 0.74, 0.7 1; 0.36, 0.19, 0.17, 0.03, 0.05, 0.10, 0.00, 0.02, 0.02, 0.02, 0.13, 0.94, 0.98, 0.89, 0.20, 0.98, 0.98, 0.97, 0.98, 0.97, 0.98, 0.98, 0.98

[0151] T4={0.68, 0.72, 0.72, 0.68, 0.70, 0.78, 0.75, 0.74, 0.67, 0.58, 0.56, 0. 51, 0.46, 0.48, 0.45, 0.43, 0.41, 0.36, 0.32, 0.28, 0.24, 0.24, 0.26, 0.2 7; 0.98, 0.97, 0.97, 0.97, 0.20, 0.97, 0.21, 0.82, 0.70, 0.58, 0.58, 0.21 ,0.20,0.23,0.05,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.03,0.10}

[0152] T5={0.09, 0.08, 0.07, 0.07, 0.08, 0.08, 0.07, 0.07, 0.06, 0.06, 0.06, 0. 06, 0.06, 0.06, 0.06, 0.08, 0.08, 0.09, 0.09, 0.11, 0.11, 0.11, 0.11, 0.1 2; 0.37, 0.35, 0.27, 0.16, 0.15, 0.10, 0.08, 0.07, 0.03, 0.02, 0.01, 0.00 ,0.01,0.00,0.00,0.00,0.07,0.03,0.04,0.03,0.00,0.00,0.98,0.98}

[0153] S04: Based on the results of cluster analysis, calculate the cluster-weighted peak-shaving demand coefficient and the cluster-typical characteristic peak-shaving demand coefficient for offshore wind power. The results are: the cluster-weighted peak-shaving demand coefficient for offshore wind power is 0.25, and the cluster-typical characteristic peak-shaving demand coefficient is 0.98.

[0154] S05: Based on the future incremental conventional power generation in the region, conduct a peak-shaving supply capacity analysis:

[0155] S051: The main new conventional power sources in this region over the next five years will be coal-fired power and electrochemical energy storage, with 4 million kilowatts of coal-fired power and 3 million kilowatts of electrochemical energy storage.

[0156] S052: The maximum and minimum technical output of coal-fired power plants are 100% and 20% respectively, and the maximum and minimum technical output of electrochemical energy storage are 100% and -100% respectively (negative numbers indicate discharge).

[0157] S053: Total peak-shaving supply within the calculation area It is 9.2 million kilowatts.

[0158] S06: Establish a wind power carrying capacity analysis model, and calculate the wind power carrying capacity range by combining regional peak-shaving demand and peak-shaving supply.

[0159] S061: Based on the load characteristics within the region, select the typical daily load characteristics in spring and autumn, take the annual maximum load as the benchmark, and set the peak-valley difference at 0.3, calculate the load peak-shaving power within the region. =350 × 0.3 = 1,050,000 kilowatts.

[0160] S062: Based on the cluster analysis results, calculate the cluster-weighted wind power anti-peak regulation coefficient. =0.25.

[0161] S063: Taking the wind power characteristics with the largest peak-to-valley difference in the fifth cluster as representative, calculate the typical anti-peak-shaving coefficient of wind power cluster for wind power anti-peak demand. =0.98.

[0162] S064: Establishing the total peak-shaving demand Computational model, The wind power counter-peak regulation capacity is determined by the wind power carrying capacity. Wind power anti-peak regulation coefficient This means, that is: (Including wind power clustering weighted anti-peak-shaving coefficient) =0.25 and the typical anti-peak-shaving coefficient of wind power clustering =0.98).

[0163] S065: Based on the principle that total peak-shaving supply is greater than or equal to total peak-shaving demand Under the constraints, two types of anti-peak-shaving coefficients are used to calculate two wind power carrying capacities, namely wind power clustering weighted carrying capacity. =32.6 million kilowatts, typical characteristics of wind power clustering carrying capacity =8.32 million kilowatts.

[0164] S066: The wind power carrying capacity of this region is expected to be approximately [832,3260] kilowatts over the next five years.

[0165] Example 3

[0166] This embodiment also proposes a wind power carrying capacity analysis system based on anti-peak shaving characteristics, used to implement the wind power carrying capacity analysis method based on anti-peak shaving characteristics proposed in Embodiment 1, including:

[0167] The data acquisition module is used to acquire historical wind power operation datasets for the area under test.

[0168] The per-unit processing module is used to perform per-unit processing on the historical wind power operation dataset to obtain a per-unitized daily wind power output characteristic dataset.

[0169] The clustering analysis module is used to perform clustering analysis on the normalized daily wind power output characteristic dataset to obtain clustering results. The clustering results include cluster centers, as well as the normalized daily wind power output characteristic data and their quantity represented by each cluster center.

[0170] The anti-peak-shaving coefficient module is used to calculate the wind power cluster weighted anti-peak-shaving coefficient and the wind power cluster typical characteristic anti-peak-shaving coefficient based on the clustering results.

[0171] The total supply calculation module is used to calculate the total peak-shaving supply in the area under test based on the installed capacity information of various types in the area under test.

[0172] The carrying capacity calculation module is used to calculate the wind power cluster weighted carrying capacity and the wind power cluster typical characteristic carrying capacity based on the constraint that the total peak-shaving supply is greater than or equal to the total peak-shaving demand, and the wind power cluster weighted anti-peak-shaving coefficient and the wind power cluster typical characteristic anti-peak-shaving coefficient, respectively.

[0173] The interval confirmation module is used to obtain the wind power carrying capacity interval of the area to be tested based on the weighted carrying capacity of wind power clustering and the carrying capacity of typical characteristics of wind power clustering.

[0174] The specific functions of each module described above are explained in the relevant content of the method in Embodiment 1, and will not be repeated here.

[0175] Example 4

[0176] This invention provides a computer-readable storage medium storing a computer program thereon, characterized in that, when the computer program is executed by a processor, it implements the steps of the wind power carrying capacity analysis method based on anti-peak shaving characteristics as described in Embodiment 1.

[0177] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0178] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0179] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0180] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0181] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A method for analyzing wind power carrying capacity based on anti-peak-shaving characteristics, characterized in that, include: Obtain the historical wind power operation dataset for the area to be tested; The historical wind power operation dataset is normalized to obtain a normalized daily wind power output characteristic dataset; Cluster analysis is performed on the normalized daily wind power output characteristic dataset to obtain cluster results. The cluster results include cluster centers, as well as the normalized daily wind power output characteristic data and their quantity represented by each cluster center. Based on the clustering results, calculate the wind power cluster weighted anti-peak-shaving coefficient and the wind power cluster typical characteristic anti-peak-shaving coefficient; Based on the installed capacity information of various types in the area to be tested, calculate the total peak-shaving supply in the area to be tested; With the constraint that the total peak-shaving supply is greater than or equal to the total peak-shaving demand, the wind power cluster weighted carrying capacity and the wind power cluster typical characteristic carrying capacity are calculated based on the wind power cluster weighted anti-peak-shaving coefficient and the wind power cluster typical characteristic anti-peak-shaving coefficient, respectively. The wind power carrying capacity range of the area to be tested is obtained based on the weighted carrying capacity of wind power clustering and the carrying capacity of typical characteristics of wind power clustering. The formula for calculating the wind power clustering weighted anti-peak-shaving coefficient is as follows: ； ； In the formula, Represents the weighted anti-peak-shaving coefficient for wind power clustering; i represents the sequence number; The anti-peak-shaving coefficient represents the wind power characteristics of the i-th cluster center; This represents the number of normalized daily wind power output characteristic data represented by the i-th cluster center; This represents the total amount of daily wind power output characteristic data in the normalized daily wind power output characteristic dataset; This represents the maximum output coefficient of the wind power characteristics of the i-th cluster center; The minimum output coefficient of the wind power characteristics of the i-th cluster center is represented; the maximum output coefficient of the wind power characteristics of the i-th cluster center is the maximum value in the wind power output curve corresponding to the i-th cluster center, and the minimum output coefficient of the wind power characteristics of the i-th cluster center is the minimum value in the wind power output curve corresponding to the i-th cluster center. The formula for calculating the anti-peak-shaving coefficient, a typical characteristic of wind power clustering, is as follows: ； In the formula, Indicates the anti-peak-shaving coefficient, a typical characteristic of wind power clustering; The maximum output coefficient representing the wind power characteristics of a typical cluster center; The minimum output coefficient represents the wind power characteristics of a typical cluster center; the typical cluster center is the cluster center with the largest number of daily wind power output characteristic data after per-unit scaling; the maximum output coefficient of the wind power characteristics of a typical cluster center is the maximum value in the wind power output curve corresponding to the typical cluster center, and the minimum output coefficient of the wind power characteristics of a typical cluster center is the minimum value in the wind power output curve corresponding to the typical cluster center. The step of calculating the total peak-shaving supply in the area under test based on the installed capacity information of various types in the area under test includes: Obtain the installed capacity information of the area to be tested, including the installed capacity type of the units and the installed capacity of different types of units; Based on the installed capacity information of the area to be tested, calculate the maximum and minimum power output coefficients of units of different installed capacity types; Based on the maximum and minimum power output coefficients of different installed capacity types, as well as the installed capacity scale information, calculate the total peak-shaving supply in the area to be tested. The formula for calculating the total peak-shaving supply is as follows: ； ； ； In the formula, Indicates the total supply for peak shaving; This represents the total maximum output of all installed types; This represents the total minimum output of all installed types; j represents the sequence number; This represents the installed capacity of the unit of type j; This represents the maximum technical output coefficient of the unit of the j-th type of installed capacity. This represents the minimum technical output coefficient of the unit of type j; The process of using the constraint that the total peak-shaving supply is greater than or equal to the total peak-shaving demand, and calculating the wind power cluster-weighted carrying capacity and the wind power cluster-typical characteristic carrying capacity based on the wind power cluster-weighted anti-peak-shaving coefficient and the wind power cluster-typical characteristic anti-peak-shaving coefficient, respectively, includes: Establish a model for calculating total peak-shaving demand; Based on the aforementioned peak-shaving total demand calculation model and the constraint that the total peak-shaving supply is greater than or equal to the total peak-shaving demand, the wind power cluster weighted carrying capacity and the wind power cluster typical characteristic carrying capacity are solved. The calculation model for total peak-shaving demand is expressed as follows: ； ； in, Indicates the total demand for peak shaving; Indicates the wind power reverse peak shaving capacity; This indicates the typical characteristics and carrying capacity of wind power clusters; This represents the weighted carrying capacity of wind power clusters. This represents the wind power anti-peak shaving coefficient, including the wind power cluster weighted anti-peak shaving coefficient or the wind power cluster typical characteristic anti-peak shaving coefficient; The formulas for solving the weighted carrying capacity of wind power clusters and the carrying capacity of typical characteristics of wind power clusters are expressed as follows: ； 。 2. The wind power carrying capacity analysis method based on anti-peak shaving characteristics according to claim 1, characterized in that, The acquisition of the historical wind power operation dataset for the area to be tested includes: Within a preset historical time period, the actual wind power output of the area to be tested is sampled at fixed frequency intervals to obtain the actual wind power output at different time points. By combining the actual wind power output at different time points on a daily basis, multiple days of wind power output characteristic data are obtained. Historical wind power operation datasets were obtained based on multiple daily wind power output characteristic data.

3. The wind power carrying capacity analysis method based on anti-peak shaving characteristics according to claim 2, characterized in that, The formula for the per-unit processing is expressed as follows: ； In the formula: This represents the wind power output characteristic data for the m-th day after standardization; This represents the wind power output characteristic data for day m. This indicates the installed capacity of the wind turbines in the area to be tested in the wind farm.

4. The wind power carrying capacity analysis method based on anti-peak shaving characteristics according to claim 3, characterized in that, Cluster analysis of the standardized daily wind power output characteristic dataset includes: S01: Preset multiple cluster centers; S02: Calculate the Euclidean distance between all the normalized daily wind power output characteristic data and each cluster center in the normalized daily wind power output characteristic dataset; S03: Based on the Euclidean distance, classify all the normalized daily wind power output characteristic data to obtain the classification results; the classification results include: the normalized daily wind power output characteristic data and their quantity represented by each cluster center; S04: Update each cluster center according to the classification results to obtain multiple updated cluster centers; S05: Calculate the similarity between each cluster center and its corresponding updated cluster center; S06: If the similarity between each cluster center and the corresponding updated cluster center is not greater than the preset threshold, then the updated cluster center replaces the cluster center, and S02-S06 are repeated until the similarity is greater than the preset threshold. If the similarity between each cluster center and its corresponding updated cluster center is greater than a preset threshold, then the updated cluster center replaces the original cluster center, and steps S02-S03 are executed to obtain the updated classification result. The updated classification result and the updated cluster center are then used as the clustering result.

5. The wind power carrying capacity analysis method based on anti-peak shaving characteristics according to claim 4, characterized in that, The wind power carrying capacity range of the area to be tested is represented as: [ , ]; in, This represents the maximum value of the typical characteristics carrying capacity of wind power clusters; This represents the maximum value of the weighted carrying capacity of wind power clustering.

6. A wind power carrying capacity analysis system based on anti-peak shaving characteristics, implementing the wind power carrying capacity analysis method based on anti-peak shaving characteristics as described in any one of claims 1-5, characterized in that, include: The data acquisition module is used to acquire historical wind power operation datasets for the area under test. The per-unit processing module is used to perform per-unit processing on the historical wind power operation dataset to obtain a per-unitized daily wind power output characteristic dataset. The clustering analysis module is used to perform clustering analysis on the normalized daily wind power output characteristic dataset to obtain clustering results. The clustering results include cluster centers, as well as the normalized daily wind power output characteristic data and their quantity represented by each cluster center. The anti-peak-shaving coefficient module is used to calculate the wind power cluster weighted anti-peak-shaving coefficient and the wind power cluster typical characteristic anti-peak-shaving coefficient based on the clustering results. The total supply calculation module is used to calculate the total peak-shaving supply in the area under test based on the installed capacity information of various types in the area under test. The carrying capacity calculation module is used to calculate the wind power cluster weighted carrying capacity and the wind power cluster typical characteristic carrying capacity based on the constraint that the total peak-shaving supply is greater than or equal to the total peak-shaving demand, and the wind power cluster weighted anti-peak-shaving coefficient and the wind power cluster typical characteristic anti-peak-shaving coefficient, respectively. The interval confirmation module is used to obtain the wind power carrying capacity interval of the area to be tested based on the weighted carrying capacity of wind power clustering and the carrying capacity of typical characteristics of wind power clustering.

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