A method and system for evaluating regional wind and solar power output complementarity and optimizing grid-connected capacity

Abstract

The present invention discloses a method and system for evaluating the complementarity of regional wind-solar output and optimizing grid-connected capacity, including: obtaining the wind power output and photovoltaic output of a target area, and obtaining a wind-solar output matrix according to the wind power output and photovoltaic output; clustering the wind-solar output matrix to obtain weather types, and extracting typical days of weather types from the wind-solar matrix for different weather types; evaluating the wind-solar output complementarity characteristics of typical days of different weather types based on the wind-solar output and photovoltaic output on typical days of weather types; constructing a wind-solar grid-connected capacity ratio optimization model, and optimizing and solving the wind-solar grid-connected capacity ratio optimization model based on the wind power output and photovoltaic output on typical days of weather types to obtain the optimal wind-solar grid-connected capacity ratio for different weather types.

Description

Technical Field

[0001] The present invention belongs to the technical field of renewable energy power generation, and in particular relates to a method and system for evaluating regional wind-solar power output complementarity and optimizing grid-connected capacity. Background Art

[0002] In the field of renewable energy, wind and solar energy are important clean energy sources, and the research and application of their combined power generation systems are increasingly valued. However, most current studies focus on the single evaluation of wind-solar complementarity or the single study of wind-solar capacity ratio optimization methods. These studies often ignore the impact of different weather conditions on the performance of wind-solar combined power generation systems in practical applications. Specifically, there are few studies on the optimal scheduling of wind-solar combined power generation systems, especially based on classification studies of different weather types.

[0003] The existing problems and shortcomings mainly include: on the one hand, the existing technology lacks in-depth analysis of the complementary characteristics of wind and solar power output under different weather types, resulting in inaccurate evaluation results and inability to effectively guide the operation and optimization of actual power generation systems; on the other hand, the existing wind and solar grid-connected capacity optimization methods often do not consider the impact of weather changes on power generation efficiency, resulting in low efficiency of optimization results in practical applications and the existing technology fails to effectively integrate wind and solar power output complementarity with grid-connected capacity optimization, resulting in uneconomical system operation. Summary of the invention

[0004] In order to solve the above technical problems, the present invention proposes a method and system for evaluating the complementarity of regional wind and solar power output and optimizing grid-connected capacity to solve the problems existing in the above-mentioned prior art.

[0005] To achieve the above object, the present invention provides a method for evaluating the complementarity of regional wind and solar power output and optimizing grid-connected capacity, comprising:

[0006] Obtain the wind power output and photovoltaic output of the target area, and obtain the wind and photovoltaic output matrix based on the wind power output and photovoltaic output;

[0007] Performing clustering processing on the wind-solar output matrix to obtain weather types, and extracting typical days of weather types from the wind-solar output matrix for different weather types;

[0008] Based on the wind power output and photovoltaic output on typical days of different weather types, the complementary characteristics of wind and photovoltaic output on typical days of different weather types are evaluated.

[0009] An optimization model for the ratio of wind-solar grid-connected capacity is constructed. The optimization model is optimized and solved based on the wind power output and photovoltaic output on typical days of weather types to obtain the optimal ratio of wind-solar grid-connected capacity in different weather types.

[0010] Optionally, the process of obtaining the wind power output and photovoltaic output of the target area includes:

[0011] Obtain meteorological data and power station characteristic data of the target area, and calculate wind power output and photovoltaic output based on the meteorological data and power station characteristic data.

[0012] Optionally, the process of obtaining the wind and solar power output matrix includes:

[0013] The wind power output and the photovoltaic output are respectively standardized, wherein the standardization adopts a maximum-minimum value standardization method;

[0014] The standardized wind power output and photovoltaic output are arranged according to time, and the results of the arrangement are combined to obtain a wind and photovoltaic output matrix.

[0015] Optionally, after obtaining the wind power output and photovoltaic output of the target area, the following steps are further included:

[0016] Standardizing and chronologically arranging the wind power output and photovoltaic output;

[0017] The wind power output and photovoltaic output are respectively averaged according to the daily time to obtain the daily output data; the daily output data are averaged according to the monthly time to obtain the monthly output data, and the long-term complementary law of wind and solar power processing is evaluated based on the daily output data and the monthly output data.

[0018] Optionally, the process of clustering the wind-solar output matrix includes:

[0019] Through the silhouette coefficient method, the optimal K value is obtained by clustering the wind and solar output matrix using the K-means algorithm;

[0020] Based on the optimal K value, the K-means algorithm is used to cluster the wind-solar output matrix to obtain the clustering results. According to the clustering results, the clusters to which different samples in the wind-solar output matrix belong are obtained, where each cluster corresponds to a weather type.

[0021] Based on different weather types, the Euclidean distance is used to calculate the average distance between each sample and other samples in the corresponding cluster, and the sample with the smallest average distance in each cluster is taken as the typical day of the corresponding weather type under the cluster.

[0022] Optionally, the process of obtaining the wind-solar output complementary characteristics includes:

[0023] Calculate the fluctuation complementarity rate for wind power output and photovoltaic output on typical days of weather type:

[0024]

[0025] Among them, D1 and D2 are the proportions of wind power and photovoltaic power in the combined power generation system, γt,1 and γ t,2 are the fluctuations of wind power and photovoltaic power output at time t; n is the total number of time; The wind and solar output fluctuation complementarity rate is between 0 and 1. The larger the value of is, the stronger the complementarity between wind power and photovoltaic output fluctuations is;

[0026] Calculate the climbing complement ratio:

[0027]

[0028] Among them, G1 and G2 are the absolute mean values ​​of the per-unit values ​​of wind power and photovoltaic output ramp rates over a period of time; R The average ramp rate of wind power and photovoltaic power combined output over a period of time; is the wind and solar output ramp complementarity rate, which is between 0 and 1. The larger the value is, the stronger the complementarity between wind power and photovoltaic output climbing is.

[0029] Optionally, the wind-solar grid-connected capacity ratio optimization model includes an objective function and constraints, wherein the objective function includes a maximization function of a fluctuation complementarity rate and a climbing complementarity rate, and the constraints include that the sum of the proportion of wind power generation and the proportion of photovoltaic power generation is 1, and the fluctuation complementarity rate and the climbing complementarity rate are in the range of (0, 1).

[0030] Optionally, the wind-solar grid-connected capacity ratio optimization model is optimized and solved by using the secretary vulture optimization algorithm and the enumeration method to refer to each other.

[0031] In order to better achieve the above technical objectives, the present invention provides a regional wind-solar output complementarity evaluation and grid-connected capacity optimization system for executing the above method.

[0032] Compared with the prior art, the present invention has the following advantages and technical effects:

[0033] 1. Improved the accuracy of wind-solar output complementarity evaluation. By introducing weather classification, the present invention can more carefully analyze the complementary characteristics of wind-solar output under different weather conditions, making the evaluation results more in line with the actual situation.

[0034] 2. Optimize the wind and solar grid-connected capacity ratio and improve the economic benefits of the power generation system. The optimization model provided by the present invention can automatically adjust the capacity ratio of wind and solar power generation according to different weather types, thereby maximizing the power generation efficiency and stability of the system.

[0035] 3. Reduce the computational complexity and time cost of optimization scheduling. Compared with the traditional single optimization algorithm, the method of mutual verification of the snake vulture optimization algorithm and the enumeration method adopted in the present invention can greatly improve the efficiency of optimization scheduling while ensuring the quality of the solution.

[0036] 4. Enhanced environmental adaptability and reliability of wind-solar combined power generation system. Through the method of the present invention, the wind-solar combined power generation system can better adapt to different weather conditions, reduce dependence on a single energy source, and improve the reliability and risk resistance of the system.

[0037] 5. In terms of social benefits, the present invention helps to promote the widespread application of renewable energy, promote the optimization of energy structure and environmental protection. By improving the utilization efficiency of wind and solar power generation, the present invention helps to reduce the consumption of fossil energy and reduce greenhouse gas emissions, which has positive significance for achieving sustainable development.

[0038] In summary, the present invention not only has significant technical effects, but also has good social and economic benefits, and provides strong technical support for the optimal scheduling of wind-solar combined power generation systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0040] Figure 1 It is a technical flow chart of an embodiment of the present invention;

[0041] Figure 2 The K-means algorithm flow chart of the embodiment of the present invention;

[0042] Figure 3 This is a curve diagram of the wind and solar power generation process according to an embodiment of the present invention;

[0043] Figure 4 This is a monthly wind and solar power output process diagram of an embodiment of the present invention;

[0044] Figure 5 A schematic diagram of clustering results based on the silhouette coefficient method according to an embodiment of the present invention;

[0045] Figure 6 It is a 24-hour output process curve diagram of a typical day under different weather types according to an embodiment of the present invention. DETAILED DESCRIPTION

[0046] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0047] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0048] In view of the problems existing in the prior art, the purpose of the present invention is to propose a method and system for evaluating the complementarity of regional wind and solar power output and optimizing grid-connected capacity, so as to solve the following technical problems:

[0049] 1. The prior art lacks in-depth analysis of the complementary characteristics of wind and solar output under different weather types, resulting in inaccurate evaluation results and inability to effectively guide the operation and optimization of actual power generation systems. The present invention aims to accurately evaluate the complementary laws of wind and solar output through in-depth research on different weather types, and improve the practicality and accuracy of the evaluation system.

[0050] 2. Existing wind-solar grid-connected capacity optimization methods often do not consider the impact of weather changes on power generation efficiency, resulting in low efficiency of optimization results in practical applications. The present invention aims to optimize the wind-solar grid-connected capacity ratio in combination with weather classification to improve the overall efficiency and stability of the power generation system.

[0051] 3. The optimization methods in the prior art are complex and time-consuming, which increases the cost of optimization scheduling. The present invention aims to provide a more efficient calculation method to reduce the time and resource consumption of the optimization process, reduce costs, and improve the feasibility of the optimization strategy.

[0052] 4. The existing technology fails to effectively integrate the complementarity of wind and solar power output and the optimization of grid-connected capacity, resulting in the system operation being not economical. The present invention aims to achieve efficient utilization of wind and solar power output and improve the economy and reliability of the system through an integrated evaluation and optimization method.

[0053] By achieving the above objectives, the present invention aims to provide a more accurate, efficient and economical optimization scheduling scheme for the regional wind-solar combined power generation system, thereby effectively improving the operating performance of the wind-solar power generation system.

[0054] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0055] Step S1, constructing a wind and solar power output calculation model.

[0056] The present invention adopts the following technical means to construct a wind and solar power output calculation model:

[0057] Step S11, collecting hourly meteorological data and power station characteristic data of the study area.

[0058] Step S12: According to meteorological data and power station characteristics, wind power and photovoltaic power output calculation formulas are established respectively.

[0059] Step S13: performing maximum-minimum value normalization processing on wind power and photovoltaic output data.

[0060] Step S14: Combining and arranging the standardized wind power and photovoltaic output data to construct an output matrix.

[0061] Step S2: Long-term wind and solar power output complementarity evaluation plan.

[0062] The present invention uses the following technical means to evaluate the long-term complementarity of wind and solar power output:

[0063] Step S21, averaging the standardized hourly wind power output and hourly photovoltaic output to obtain the daily output.

[0064] Step S22: average the daily wind power output and the daily photovoltaic output to obtain the monthly output.

[0065] Step S3: Weather type classification and typical day extraction scheme.

[0066] The present invention adopts the following technical means to classify weather types and extract typical days:

[0067] Step S31: Use the silhouette coefficient method to determine the optimal number of clusters K of the K-means algorithm.

[0068] Step S32: Apply K-means algorithm to cluster the output data, divide different weather types and extract typical days of each type.

[0069] Step S4: constructing a wind-solar power complementarity evaluation model.

[0070] The present invention adopts the following technical means to construct a wind-solar output complementary evaluation model:

[0071] The complementarity of wind and solar power output is evaluated by calculating the fluctuation complementarity rate and the climbing complementarity rate.

[0072] The calculation formula of volatility complementarity is as follows:

[0073]

[0074] Where: D1 and D2 are the proportions of wind power and photovoltaic power in the combined power generation system, γ t,1 and γ t,2 are the fluctuations of wind power and photovoltaic power output at time t; n is the total number of time; The wind and solar output fluctuation complementarity rate is between 0 and 1. The larger the value of , the stronger the complementarity between wind power and photovoltaic output fluctuations.

[0075] The calculation formula of climbing complementarity rate is as follows:

[0076]

[0077]

[0078]

[0079] Where: G1 and G2 are the absolute mean values ​​of the per-unit values ​​of wind power and photovoltaic output ramp rates over a period of time; M1 and β i,1 are the number of wind power output ramping events in a period of time and the per unit value of the ramping rate of the ith ramping event. This paper defines a ramping event as the absolute difference between the maximum and minimum output values ​​within a 1h time window is greater than 20% of the rated installed capacity; M2 and β i,2 are the number of photovoltaic output ramp events and the per unit value of the ramp rate of the ith ramp event within a period of time; R The average ramp rate of wind power and photovoltaic power combined output over a period of time; is the wind and solar output ramp complementarity rate, which is between 0 and 1. The larger the value is, the stronger the complementarity between wind power and photovoltaic output climbing is.

[0080] Step S5: constructing a wind-solar grid-connected capacity ratio optimization model.

[0081] Step S51, the present invention adopts the following technical means to construct a wind-solar grid-connected capacity ratio optimization model:

[0082] Step S511: Establish an objective function with the goal of maximizing the fluctuation complementarity rate and the climbing complementarity rate:

[0083]

[0084]

[0085] Step S512, setting constraints, including restrictions on the proportion of wind power generation and photovoltaic power generation, and requirements on fluctuation complementarity ratio and ramp complementarity ratio (step S512).

[0086] The proportion of wind power generation D1 and the proportion of photovoltaic power generation D2 should meet the following requirements:

[0087]

[0088] Volatility Complementarity Complementary rate of climbing Should meet:

[0089]

[0090]

[0091] Step S52: solve the optimization model by using the snake vulture optimization algorithm and the enumeration method to obtain the optimal wind-solar grid-connected capacity ratio.

[0092] The technical solution of the present invention can effectively solve the problems in the prior art and improve the operating performance and economy of the wind-solar combined power generation system through the above steps and means.

[0093] The above technical solution is described in detail with reference to the accompanying drawings:

[0094] like Figure 1 As shown, a method and system for evaluating regional wind-solar power output complementarity and optimizing grid-connected capacity provided by the present invention mainly includes the following steps:

[0095] Step S1, constructing a wind and solar power output calculation model, calculating the wind and solar power output, and constructing an output matrix after standardization;

[0096] Step S2: Evaluate the long-term complementarity of wind and solar outputs based on the output calculation results;

[0097] Step S3, dividing weather types according to the output matrix and extracting typical days;

[0098] Step S4: construct a wind-solar power complementarity evaluation model to evaluate the wind-solar power complementarity characteristics on typical days of different weather types;

[0099] Step S5: construct a wind-solar grid-connected capacity ratio optimization model to obtain the optimal wind-solar grid-connected capacity ratio.

[0100] Specifically, the process of step S1, constructing a wind and solar power output calculation model and constructing an output matrix includes:

[0101] Step S11, collecting meteorological data and power station characteristic data of the research area.

[0102] First, meteorological data (such as wind speed, light intensity, temperature, etc.) and power station characteristic data (such as wind turbine model, photovoltaic module parameters, and the proportion of wind power and photovoltaic power in the combined power generation system) are collected from the meteorological stations and wind and photovoltaic power stations in the study area at preset time periods.

[0103] Step S12: Constructing a wind and solar power output calculation model

[0104] Based on the collected data, the hourly wind power and photovoltaic output are calculated using the following formula:

[0105] Wind power output calculation formula:

[0106]

[0107] Where: N Wis wind power output, W; ρ is air density, kg / ; A W is the swept area of ​​turbine blades; λ is the wind energy utilization coefficient; v is the wind speed, m / s.

[0108] Photovoltaic output calculation formula:

[0109]

[0110] Where: P i Photovoltaic output, W, P max is the installed capacity of the photovoltaic power station, 10,000 kW; R i is the solar radiation intensity, W / m 2 ; T i is the solar panel temperature, ℃; R stc is the solar radiation intensity under standard test conditions, W / m 2 ; T stc is the air temperature under standard test conditions, ℃; α p is the air temperature to power conversion coefficient.

[0111] When calculating, some parameters are taken as: P max Take 16.01 million kW for calculation; R stc 1000 W / m 2 ; T stc is 25℃; p Take -0.35% / ℃.

[0112] Since weather stations usually cannot provide temperature data for solar panels, the air temperature provided by the weather station can be converted to the temperature of the solar panels as follows:

[0113]

[0114] Where: T air,i The temperature provided by the weather station, ℃; T noc The normal operating temperature of the solar panel is usually 48 2℃.

[0115] Step S13: Output standardization

[0116] The maximum-minimum value normalization method is used to normalize the hourly wind power and photovoltaic output data. The specific formula is as follows:

[0117]

[0118] Where: E new is the standardized data, ranging from [0,1]; E raw is the original data; E max and Emin are the maximum and minimum values ​​of the original data respectively.

[0119] Step S14: Constructing an output matrix

[0120] The standardized hourly output is arranged according to time, and the wind power output and photovoltaic output are combined together to construct a wind and solar output matrix.

[0121] Specifically, step S2, evaluating the long-term complementarity of wind and solar power output in the study area, includes:

[0122] Step S21: Calculate daily wind power output and photovoltaic power output

[0123] The long series of standardized hourly wind power output and hourly photovoltaic output are averaged to obtain daily output data;

[0124] Step S22: Calculate monthly wind power output and photovoltaic output

[0125] The standardized daily data are averaged to obtain the monthly output data.

[0126] Through the calculation of step S21 and step S22, the average output data of wind power and photovoltaic power on a daily and monthly time scale are obtained. By comparing the wind power and photovoltaic power output data of different months, the complementarity of wind power and photovoltaic power output in different seasons is analyzed. For example, in some seasons, wind power may be stronger and photovoltaic power is weaker, while in other seasons it may be the opposite. By analyzing the daily data, the long-term trend and stability of wind power and photovoltaic power output are evaluated to determine the complementarity of the two on a long-term time scale. Through these analyses, the complementarity law of wind power and photovoltaic power on a long-term time scale can be obtained, which provides a scientific basis for the optimal design and operation of the wind-solar hybrid energy system, thereby improving the stability and economic benefits of the entire energy system.

[0127] Specifically, step S3, classifying weather types and extracting typical days, includes:

[0128] Step S31, determine the K value:

[0129] The silhouette coefficient method is used to obtain the K value with the best classification effect when using the K-means algorithm for clustering. The calculation formula is:

[0130]

[0131] Where: A i Indicates the calculation of the i-th data X i The average distance to other data in the same cluster; B i Indicates selection of X i The nearest average distance obtained by any cluster B outside. i, Z i Represents the overall classification effect, Z i The value range of is [-1,1]. The closer it is to 1, the better the classification effect. i The value of Z i The value of K at maximum.

[0132] Step S32: Apply K-means algorithm clustering

[0133] The K-means algorithm is used to cluster the hourly wind and solar power output matrix, divide different weather types, and extract typical days of each type. The K-means algorithm flow chart is as follows: Figure 2 As shown, the following steps are included:

[0134] Step S321, initializing cluster centers: randomly selecting K samples from the wind and solar power output matrix as initial cluster centers.

[0135] Step S322, iterative process, repeating the following steps until the cluster center no longer changes or reaches a preset number of iterations, including:

[0136] Step S322a: For each sample, calculate the distance between it and each cluster center.

[0137] Step S322b: assign each sample to the cluster represented by the nearest cluster center.

[0138] Step S322c: for each cluster, recalculate the cluster center, that is, the mean of all samples in the cluster.

[0139] Step S323: extracting typical days of various types, including:

[0140] Step S323a, determine the clustering result, that is, determine the cluster to which each sample belongs based on the final cluster center, and each cluster corresponds to a weather type.

[0141] Step S322b: Calculate the similarity of samples within the cluster, and use the Euclidean distance to calculate the average distance between each sample and other samples within the cluster. Select the sample with the smallest average distance, i.e., the largest similarity, from each cluster as the typical day of the cluster.

[0142] Specifically, step S4, constructing a wind-solar power complementarity evaluation model and evaluating the wind-solar power complementarity characteristics under typical days of different weather types, wherein the subsequent calculation of the complementarity rate is based on the time period under the typical day of the weather type, including:

[0143] Step S41, evaluating the wind-solar output complementarity through the fluctuation complementarity rate and the climbing complementarity rate, including:

[0144] Step S411, calculating the volatility complementarity rate:

[0145] The volatility complementarity rate is used to evaluate the degree of reduction in output fluctuations when wind power and photovoltaic power are jointly generated. This indicator measures the effect of combined power generation on reducing volatility by comparing the fluctuations when wind power and photovoltaic power are jointly generated with those when each is generated separately. The calculation formula is as follows:

[0146]

[0147] Where: D1 and D2 are the proportions of wind power and photovoltaic power in the combined power generation system, γ t,1 and γ t,2 are the fluctuations of wind power and photovoltaic power output at time t; n is the total number of time; The wind and solar output fluctuation complementarity rate is between 0 and 1. The larger the value of , the stronger the complementarity between wind power and photovoltaic output fluctuations.

[0148] The specific process includes: Step S411a, for each time t, calculate the wind power output fluctuation γ t,1 The output fluctuation of the photovoltaic power t,2 The fluctuation is the absolute value of the difference between the output at the current moment and the output at the previous moment.

[0149] Step S411b, calculate the sum of fluctuations when wind and solar power are generated alone, D1|γ t,1 |+D2|γ t,2 |

[0150] Step S411c, calculate the fluctuation amount during joint power generation. For each time t, calculate the output fluctuation amount of the joint power generation system, |D1γ t,1+ D2γ t,2 |.

[0151] Step S411d, calculate the fluctuation complementarity rate.

[0152] Step S412, calculating the slope complementation rate:

[0153] The wind-solar output ramping complementarity rate mainly measures the rate of decrease of the average ramping rate of the combined output of wind power and photovoltaic power compared with the average ramping rate of the individual output of wind power and photovoltaic power over a period of time. It can accurately characterize the ability of wind power and photovoltaic complementary power generation to reduce the output ramping degree. It is more targeted than the fluctuation complementarity rate and is mainly used for the operation and scheduling of wind-solar complementary power generation systems, which can reduce the rotating standby capacity.

[0154]

[0155] Where: G1 and G2 are the absolute mean values ​​of the per-unit values ​​of wind power and photovoltaic output ramp rates over a period of time; RThe average ramp rate of wind power and photovoltaic power combined output over a period of time; is the wind and solar output ramp complementarity rate, which is between 0 and 1. The larger the value is, the stronger the complementarity between wind power and photovoltaic output climbing is.

[0156] The specific process includes: Step S412a, calculating the absolute mean of the wind power output ramp rate per unit value. For each time window, calculate the wind power output ramp rate per unit value β i,1 :If the absolute difference between the maximum and minimum output values ​​in a time window is greater than 20% of the rated installed capacity of wind power, it is considered that a ramp event has occurred, which is recorded as β i,1 . Calculate the total number of wind power output ramp events M1 and the absolute mean value of the ramp rate per unit value G1:

[0157]

[0158] M1 and β i,1 are the number of wind power output ramping events in a period of time and the per-unit value of the ramping rate of the ith ramping event. This paper defines a ramping event as the absolute difference between the maximum and minimum output values ​​within a 1-hour time window is greater than 20% of the rated installed capacity;

[0159] Step S412b, calculate the absolute mean of the per-unit value of the photovoltaic output ramp rate. For each time window, calculate the per-unit value of the photovoltaic output ramp rate β i,2 :If the absolute difference between the maximum and minimum output values ​​in a time window is greater than 20% of the rated installed capacity of photovoltaic power generation, it is considered that a ramp event has occurred and β is recorded. i,2 Calculate the total number of photovoltaic output ramp events N2 and the absolute mean value G2 of the ramp rate per unit value:

[0160]

[0161] M2 and β i,2 are the number of photovoltaic output ramp events within a period of time and the per unit value of the ramp rate of the i-th ramp event;

[0162] Step S412c, calculating the absolute mean value of the per unit value of the ramp rate during joint power generation R For each time window, the combined output of wind power and photovoltaic power is calculated first. If the absolute difference between the maximum and minimum output values ​​in a time window is greater than 20% of the rated installed capacity of wind and photovoltaic power, it is considered that a ramp event has occurred and the β i,3 .

[0163] Calculate the total number of wind-solar combined output ramp events M3 and the absolute mean of the ramp rate per unit value O R :

[0164]

[0165] O R is the average ramp rate of wind power and photovoltaic power combined output over a period of time, β i,3 are the per unit values ​​of the ramp rate of the i-th ramp event of the combined output of wind power and photovoltaic power within a period of time;

[0166] Step S412d, calculating the slope complementation rate.

[0167] Step S42: Evaluate the complementary characteristics of wind and solar power output:

[0168] According to the calculation results, the wind-solar output complementarity characteristics under typical days of different weather types are evaluated. A high fluctuation complementarity rate indicates that under this weather type, the output fluctuations of wind power and photovoltaic power can compensate each other, thereby reducing the volatility of the overall system. A low fluctuation complementarity rate indicates that under this weather type, the output fluctuations of wind power and photovoltaic power may occur simultaneously, increasing the volatility of the overall system. A high climbing complementarity rate indicates that under this weather type, the output climbing of wind power and photovoltaic power can compensate each other, thereby reducing the output climbing degree of the overall system. The climbing complementarity rate indicates that under this weather type, the output climbing of wind power and photovoltaic power may occur simultaneously, increasing the output climbing risk of the overall system. Taking into account the fluctuation complementarity rate and the climbing complementarity rate, the wind-solar output complementarity characteristics of each weather type are evaluated.

[0169] Specifically, step S5, constructing a wind-solar grid-connected capacity ratio optimization model and obtaining an optimal wind-solar grid-connected capacity ratio, includes:

[0170] Step S51, determine the objective function and constraints:

[0171] Step S511: determine the objective function, as follows:

[0172] With the goal of maximizing the fluctuation complementarity rate and the climbing complementarity rate, the objective function is established and the corresponding constraints are set.

[0173]

[0174]

[0175] Step S512: determine the model constraints, as follows:

[0176] The proportion of wind power generation D1 and the proportion of photovoltaic power generation D2 should meet the following requirements:

[0177]

[0178] Volatility Complementarity Complementary rate of climbing Should meet:

[0179]

[0180]

[0181] Step S52: Solve the model to obtain the optimal grid-connected capacity ratio:

[0182] The optimization model is solved by using the snake vulture optimization algorithm and the enumeration method to obtain the optimal grid-connected capacity ratio of wind and solar power under different weather types, including the following:

[0183] Step S521: Secretary vulture optimization algorithm, specifically including the following contents:

[0184] Step S521a: Random initialization. a. Initialize the population: randomly generate a set of solutions (individuals) in the search space, each solution represents a possible wind-solar grid-connected capacity ratio (D1, D2). b. Evaluate the initial population: calculate the objective function value of each solution (the weighted sum of the fluctuation complementarity rate and the climbing complementarity rate).

[0185] Step S521b: Hunting strategy (exploration phase), divided into three phases.

[0186] The position of the secretary bird at this stage represents the proportion of wind and solar grid-connected capacity at this time, which is calculated by the following formula:

[0187]

[0188] Where, X i represents the position of the ith secretary bird in the exploration phase; Y1 represents the exploration phase; represents the position of the i-th secretary bird after the exploration phase; F i is the fitness value of the objective function of the i-th secretary bird in the current state; It represents the fitness value of the objective function of the i-th secretary bird at the end of the exploration phase.

[0189] Phase 1 (Searching for prey): The algorithm simulates the sharp eyesight of the secretary bird and searches for potential high-quality solutions by exploring the search space. The position of the secretary bird is updated according to the following formula:

[0190]

[0191] In the formula, t represents the current number of iterations; I represents the maximum number of iterations; x i,j Indicates the j-th dimension value of the i-th secretary bird in the current state; represents the value of the jth dimension at the end of the exploration phase of the i-th secretary bird, which is the new position of the secretary bird on the jth dimension; x random_1 and x random_2represents two randomly selected positions during the exploration phase iteration; Q1 represents an array of dimension 1×Dim randomly generated in the interval [0, 1], where Dim is the dimension of the solution space.

[0192] Phase 2 (consuming prey): After the secretary bird finds the snake, it will not immediately pounce into the fight like other birds of prey, but will use its agile steps to maneuver around the snake. During the predation phase, its position is updated according to the following formula:

[0193]

[0194]

[0195] In the formula, randn(1, Dim) represents an array of dimension 1×Dim randomly generated from a standard normal distribution, and x best Indicates the current optimal value.

[0196] Phase 3 (Attacking the Prey): When the snake is exhausted, the Secretary Bird looks for the right moment to act quickly and use its powerful leg muscles to attack. This phase usually involves the Secretary Bird's kicking technique, where it quickly raises its legs and accurately kicks the snake with its sharp claws, usually aiming at the snake's head. The algorithm adjusts the solution position in this phase to explore further. The position of the Secretary Bird is updated according to the following formula:

[0197]

[0198]

[0199] Where Levy(Dim) represents the Levy flight distribution function.

[0200] Step S521c: Escape strategy (development phase).

[0201] The natural enemies of secretary birds are large carnivores, such as eagles and jackals, which may attack secretary birds or steal their food. When faced with these threats, secretary birds usually adopt various escape strategies to protect themselves or their food. One of them is the escape strategy. When secretary birds are near predators, they first look for a suitable camouflage environment. If there is no suitable and safe camouflage environment nearby, they will choose to fly or run quickly. The algorithm avoids local optimality by developing known high-quality solutions at this stage. Update the position of the secretary bird according to the following formula:

[0202]

[0203]

[0204]

[0205] In the formula, Y2 represents the development stage; represents the position of the i-th secretary bird after the development stage; C1 refers to environmental camouflage; C2 refers to flying or running fast; represents the fitness value of the objective function of the i-th secretary vulture at the end of the development stage, which represents the system performance under the current wind-solar grid-connected capacity ratio, that is, the weighted sum of the fluctuation complementarity rate and the climbing complementarity rate. The weights in the weighted sum are set according to artificial experience, such as 0.5 and 0.5 respectively; r is 0.5; Q2 represents an array with a dimension of 1×Dim randomly generated from a normal distribution, x random Indicates the randomly selected position of the current iteration; H indicates the random selection of the integer 1 or 2; rand(1, 1) means randomly generating a random number between (0, 1).

[0206] Through the above steps, the secretary vulture optimization algorithm can obtain the optimal grid-connected capacity ratio of wind and solar.

[0207] Step S522: Enumeration method

[0208] Step S5221: Define a suitable step size to discretize the variables, set the step size to 0.01, and enumerate all possible proportion combinations.

[0209] Step S5222: Enumerate all possible solutions, and for each enumerated combination, calculate the fluctuation complementarity rate and the climbing complementarity rate.

[0210] According to the objective function defined previously, calculate the objective function value.

[0211] Step S5223: During the enumeration process, compare the objective function value of each solution, and record the optimal solution and its corresponding objective function value.

[0212] Step S523: Mutual verification

[0213] Compare the optimal solutions obtained by the Secretary Eagle Optimization Algorithm and the Enumeration Method. If the two methods obtain similar results, "similar results" can be defined as the relative error of the results obtained by the two methods is less than or equal to 5%, then the results can be considered similar, as determined by the following formula:

[0214]

[0215] in, represents the proportion of wind power generation in the optimal grid-connected capacity ratio obtained by the secretary vulture optimization algorithm, It represents the proportion of wind power generation in the optimal grid-connected capacity ratio obtained by the enumeration method. When the above conditions are met, it can be considered that the two methods have obtained similar results, and thus a more reliable optimal grid-connected capacity ratio has been found. If the relative error exceeds 5%, it indicates that there is a large difference in the results of the two methods. At this time, it is necessary to adjust the parameters of the secretary vulture optimization algorithm or the search range of the enumeration method according to the specific situation, and recalculate to seek more consistent results.

[0216] Step S524: respectively obtain the optimal wind-solar grid-connected capacity ratio under different weather types under the secretary vulture optimization algorithm and the enumeration method.

[0217] Example Analysis

[0218] Taking Qinghai Province as the study area, the method of the present invention is analyzed and verified. The data used in this case analysis comes from the National Solar Radiation Database, involving the meteorological data of Qinghai Province throughout 2020, with a time resolution of 1 hour. By processing meteorological data such as solar radiation, temperature, and wind speed, the wind and solar output is calculated, and the extreme value normalization method is used for standardization to construct a wind and solar output matrix with a dimension of 365×48. In 2020, the installed capacity of wind power in Qinghai Province was 8.43 million kW, the installed capacity of photovoltaic power was 16.01 million kW, and the ratio of wind and solar grid-connected capacity D1:D2 was 34:66.

[0219] By averaging the standardized hourly wind power output and photovoltaic output, we can obtain the daily and monthly wind and photovoltaic output, such as Figure 3 and Figure 4 The silhouette coefficient method is used to evaluate the classification effect of the K-means clustering algorithm. The classification effect is best when the number of clusters K=6. Figure 5 As shown. Based on this, the K-means algorithm is used for clustering to obtain six weather types with different characteristics, as shown in Table 1. Table 1 is the clustering result table of six weather types with different characteristics. Further, the typical daily 24-hour output process under each weather type is extracted, and the output process line is drawn, as shown in Figure 6 shown.

[0220] Table 1

[0221]

[0222] Using the fluctuation complementarity rate and the climbing complementarity rate as complementarity evaluation indicators, the wind-solar output complementarity law under different weather types was quantitatively evaluated. The evaluation results of the fluctuation complementarity rate and the climbing complementarity rate under different weather types are shown in Table 2. Table 2 is the evaluation results table of the fluctuation complementarity rate and the climbing complementarity rate under different weather types.

[0223] Table 2

[0224]

[0225] The optimal wind-solar grid-connected capacity ratio under different weather types, as well as the corresponding optimal fluctuation complementarity and climbing complementarity, were determined by using the secretary vulture optimization algorithm and the enumeration method, respectively. The optimal wind-solar grid-connected capacity ratio obtained by the secretary vulture optimization algorithm and the optimal wind-solar grid-connected capacity ratio obtained by the enumeration method are shown in Tables 3 and 4. The relative errors of the results obtained by the two methods are calculated to be less than 5%, which can be judged as similar results. The optimization results show that by adjusting the grid-connected capacity ratio of wind and solar energy, the complementarity and stability of the system can be improved, which provides a theoretical basis and practical guidance for the optimal scheduling of wind-solar complementary power generation systems. Table 3 shows the optimal wind-solar grid-connected capacity ratio obtained by the secretary vulture optimization algorithm, and Table 4 shows the optimal wind-solar grid-connected capacity ratio obtained by the enumeration method.

[0226] Table 3

[0227]

[0228] Table 4

[0229]

[0230] The above are only preferred specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for evaluating the complementarity of regional wind and solar power output and optimizing grid-connected capacity, characterized in that: include: Obtain the wind power output and photovoltaic output of the target area, and obtain the wind and photovoltaic output matrix based on the wind power output and photovoltaic output; Performing clustering processing on the wind-solar output matrix to obtain weather types, and extracting typical days of weather types from the wind-solar output matrix for different weather types; Based on the wind power output and photovoltaic output on typical days of different weather types, the wind-solar output complementary characteristics on typical days of different weather types are evaluated, among which the wind-solar output complementary characteristics include the fluctuation complementary rate and the climbing complementary rate; Construct a wind-solar grid-connected capacity ratio optimization model, optimize and solve the wind-solar grid-connected capacity ratio optimization model based on the wind power output and photovoltaic output on typical weather days, and obtain the optimal wind-solar grid-connected capacity ratio under different weather types. Among them, the wind-solar grid-connected capacity ratio optimization model aims to maximize the fluctuation complementarity rate and the climbing complementarity rate; The process of obtaining the complementary characteristics of wind and solar power output includes: Calculate the fluctuation complementarity rate for wind power output and photovoltaic output on typical days of weather type: Among them, D1 and D2 are the proportions of wind power and photovoltaic power in the combined power generation system, γ t,1 and γ t,2 are the fluctuations of wind power and photovoltaic power output at time t; n is the total number of time; The wind and solar output fluctuation complementarity rate is between 0 and 1. The larger the value of is, the stronger the complementarity between wind power and photovoltaic output fluctuations is; Calculate the climbing complement ratio: Among them, G1 and G2 are the absolute mean values ​​of the per unit values ​​of wind power and photovoltaic output ramp rates over a period of time; R The average ramp rate of wind power and photovoltaic power combined output over a period of time; is the wind and solar output ramp complementarity rate, which is between 0 and 1. The larger the value is, the stronger the complementarity between wind power and photovoltaic output climbing is.

2. The method according to claim 1, characterized in that The process of obtaining wind power output and photovoltaic output in the target area includes: Obtain meteorological data and power station characteristic data of the target area, and calculate wind power output and photovoltaic output based on the meteorological data and power station characteristic data.

3. The method according to claim 1, characterized in that The process of obtaining the wind and solar power output matrix includes: The wind power output and the photovoltaic output are respectively standardized, wherein the standardization adopts a maximum-minimum value standardization method; The standardized wind power output and photovoltaic output are arranged according to time, and the results of the arrangement are combined to obtain a wind and photovoltaic output matrix.

4. The method according to claim 1, characterized in that: After obtaining the wind power output and photovoltaic output of the target area, it also includes: Standardizing and chronologically arranging the wind power output and photovoltaic output; The wind power output and photovoltaic output are respectively averaged according to the daily time to obtain the daily output data; the daily output data are averaged according to the monthly time to obtain the monthly output data, and the long-term complementary law of wind and solar power processing is evaluated based on the daily output data and the monthly output data.

5. The method according to claim 1, characterized in that The process of clustering the wind and solar power output matrix includes: Through the silhouette coefficient method, the optimal K value is obtained by clustering the wind and solar output matrix using the K-means algorithm; Based on the optimal K value, the K-means algorithm is used to cluster the wind-solar output matrix to obtain the clustering results. According to the clustering results, the clusters to which different samples in the wind-solar output matrix belong are obtained, where each cluster corresponds to a weather type. Based on different weather types, the Euclidean distance is used to calculate the average distance between each sample and other samples in the corresponding cluster, and the sample with the smallest average distance in each cluster is taken as the typical day of the corresponding weather type under the cluster.

6. The method according to claim 1, characterized in that The wind-solar grid-connected capacity ratio optimization model includes objective functions and constraints, where the objective function includes the maximization function of the fluctuation complementarity rate and the climbing complementarity rate, and the constraints include that the sum of the proportion of wind power generation and the proportion of photovoltaic power generation is 1, and the fluctuation complementarity rate and the climbing complementarity rate are in the range of (0, 1).

7. The method according to claim 1, characterized in that The wind-solar grid-connected capacity ratio optimization model is optimized and solved by mutual reference of the secretary vulture optimization algorithm and the enumeration method.

8. A regional wind and solar power output complementarity evaluation and grid-connected capacity optimization system, characterized in that: Used to execute the method according to any one of claims 1 to 7.

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