Uncertainty scenario generation and planning method based on source load matching and timing characteristics

Abstract

The present application relates to the technical field of energy planning, and discloses a kind of uncertainty scene generation and planning method based on source load matching and timing characteristics, effectively reduce operating performance deviation and improve the engineering implementability of random planning scheme.The present application determines representative meteorological year data set, reference capacity optimization variable set and annual wind light load initial scene time set by the historical data of target area in previous years;Based on the positive and negative of source load difference, the sample points within each hour are divided into surplus power and power shortage state region set;Two kinds of sets are respectively clustered by distance clustering algorithm every hour, to obtain annual typical scene time set and corresponding first probability set;Further generate annual disordered boundary sequence, and optimize based on the timing of representative meteorological year data set, form annual scene boundary with timing characteristics.The method effectively retains the difference between source and load and timing characteristics, improves the accuracy of system reliability evaluation and planning implementability, reduces operating deviation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy planning, in particular to a source-load matching and time sequence characteristic based uncertainty scenario generation and planning method. BACKGROUND

[0002] With the rapid development of wind energy, light energy and other new energy power with strong randomness and volatility, how to realize large-scale and efficient consumption of new energy power has become a key issue to promote the transformation of China's energy structure. However, with the increasing penetration rate of random and intermittent power sources such as wind and light on the source side, and the increasingly complex and uncertain electricity consumption behavior on the load side, the energy system (such as wind, light, heat and storage system, distributed energy system, and integrated energy system) faces significant uncertainty challenges in the planning stage, and therefore the random planning method has attracted widespread attention.

[0003] In the current uncertainty random planning method, the scenario-based analysis method has been widely used by introducing random variables to characterize uncertainty. This method usually includes four steps: probability distribution fitting, scenario generation, scenario reduction, and construction of uncertainty boundary. The existing random planning method uses distance-based clustering algorithm (such as K-means clustering algorithm) in the scenario reduction link, but the uncertainty boundary constructed by such method has a large unreasonable, resulting in a large relative deviation of the net present value, wind and light curtailment rate and power following deviation from the actual mean value, and the engineering implementability of the random planning scheme is low. In highly nonlinear energy systems, the above limitations are particularly prominent - time-varying operation strategy may further amplify the performance deviation between planning and actual operation, resulting in significant deviation of the planning scheme based on traditional uncertainty optimization from the actual engineering effect. SUMMARY

[0004] The present application provides a source-load matching and time sequence characteristic based uncertainty scenario generation and planning method, which aims to at least solve one of the technical problems existing in the prior art. To this end, the present application proposes a source-load matching and time sequence characteristic based uncertainty scenario generation and planning method, which can distinguish the characteristics of positive / negative differences in source-load matching, and combine the time sequence correlation and operation coupling characteristics between the source and the load to effectively optimize the uncertainty boundary, improve the rationality, and thus obtain better uncertainty scenarios and planning results, effectively reduce the operation performance deviation, and effectively improve the engineering implementability of the random planning scheme.

[0005] In a first aspect, the technical solution of the present application relates to a source-load matching and time sequence characteristic based uncertainty scenario generation method, comprising the following steps:

[0006] According to the historical data of the target region in previous years, a representative meteorological year data set, a reference capacity optimization variable set and a full-year wind and light load initial scenario time set are determined respectively;

[0007] Based on the reference capacity optimization variable set, the source-load difference value of the wind and light load sample point of each hour of the full-year wind and light load initial scenario time set is calculated, and according to the positive and negative of the corresponding source-load difference value, the wind and light load sample points in each hour are divided into a surplus power state region set in which the source is greater than the load and a power shortage state region set in which the source is less than the load;

[0008] Based on the distance clustering algorithm, the wind and light load sample points in the corresponding surplus power state region set and the power shortage state region set are clustered respectively hour by hour, and after iterative convergence, a full-year typical scenario time set and a corresponding first probability set are determined;

[0009] According to the full-year typical scenario time set and the first probability set, a full-year unordered boundary sequence is determined, and based on the time sequence of the representative meteorological year data set, the full-year unordered boundary sequence is optimized according to a genetic algorithm to generate a full-year scenario boundary with time sequence characteristics.

[0010] According to some embodiments of the present application, the representative meteorological year data set is determined by historical data in previous years based on a reference year generation method or a typical year construction method.

[0011] According to some embodiments of the present application, the reference year generation method comprises the following steps:

[0012] According to the historical data of the target region in at least 5 consecutive years, the average wind speed, the annual total radiation and the total load of each year are determined;

[0013] Based on the parameters of the average wind speed, the annual total radiation and the total load of the target region in at least 5 consecutive years, the corresponding parameters of each year are sorted respectively to determine the ranking of the corresponding parameters of each year;

[0014] Based on the ranking of the corresponding parameters of each year, the comprehensive median deviation D of each year is determined;

[0015] Based on the comprehensive median deviation of each year, the historical data of the year with the smallest deviation is selected as the representative meteorological year data set; when there are multiple years with the same and smallest deviation, the year with a more moderate annual ranking is selected as the representative meteorological year data set according to a preset parameter priority order, and the parameter priority order is the average wind speed, the annual total radiation and the total load.

[0016] According to some embodiments of the present application, the typical year construction method comprises the following steps:

[0017] Preprocessing historical data of at least 10 consecutive years of the target region to determine daily average values of each month of each year of a first target parameter, the first target parameter including solar radiation, wind speed, dry-bulb temperature, and humidity;

[0018] According to the daily average values of each of the first target parameters, long-term average cumulative distribution functions of each month of each year and single-year cumulative distribution functions of each month are determined respectively;

[0019] According to the mean square errors of the single-year cumulative distribution functions and the long-term average cumulative distribution functions corresponding to each month of each year, and based on months 1 to 12, the month with the minimum mean square error corresponding to each month is selected as a target month respectively;

[0020] Hourly data of the target months corresponding to months 1 to 12 are spliced and smoothed in sequence to serve as a representative meteorological year data set.

[0021] According to some embodiments of the present application, the determination of the reference capacity optimization variable set includes the following steps:

[0022] The data of the representative meteorological year data set are taken as system operation boundaries, and a multi-objective optimization algorithm is used to optimize and solve system reference capacity configuration to determine the reference capacity optimization variable set.

[0023] According to some embodiments of the present application, the determination of the initial scenario set of annual wind-solar load includes the following steps:

[0024] Based on historical data of at least 5 consecutive years of the target region, Gaussian kernel density estimation is used to fit each of the second target parameters in each hour to determine the probability density function corresponding to each of the second target parameters in each hour, and the cumulative distribution function corresponding to each of the second target parameters in each hour is obtained by integration, the second target parameters including wind power, solar radiation, and power load;

[0025] Based on the cumulative distribution function corresponding to each hour, Frank-Copula functions are used to construct wind-solar load three-dimensional joint cumulative distribution functions corresponding to each of the second target parameters in each hour in a hierarchical manner;

[0026] Based on the wind-solar-load three-dimensional joint cumulative distribution function of each hour, Monte Carlo or Latin hypercube sampling method is used to generate joint sampling points of each hour, and inverse transformation is performed on the corresponding joint sampling points of each hour to determine the sample values corresponding to each joint sampling point in each hour, the sample values including first sample values corresponding to wind power, second sample values corresponding to solar radiation and third sample values corresponding to power load, and the corresponding first sample values, second sample values and third sample values in each hour are taken as the wind-solar-load sample points in each hour to constitute the wind-solar-load initial scene time of each hour;

[0027] The wind-solar-load initial scene times of each hour are combined to constitute a wind-solar-load initial full-day scene, and are divided into summer, transition season and winter, and the corresponding wind-solar-load initial scene time sets of 24 hours in summer, 24 hours in transition season and 24 hours in winter are generated, and all the wind-solar-load initial scene sets are combined to serve as the annual wind-solar-load initial scene set.

[0028] According to some embodiments of the present application, the determination of the reference capacity optimization variable set comprises the following steps:

[0029] Based on the distance clustering algorithm, all the wind-solar-load initial full-day scenes are clustered day by day, and after iterative convergence, a set of annual typical scene days and a corresponding second probability set are determined;

[0030] According to the index performance of each typical scene day in the set of annual typical scene days, and according to the weighted sum of the corresponding second probability set, the annual index performance CPWAB is determined.

[0031] The index performance is taken as the system operation boundary input, and based on the multi-objective optimization algorithm, the system reference capacity configuration is optimized and solved to determine the reference capacity optimization variable set.

[0032] According to some embodiments of the present application, the annual wind-solar-load initial scene set and the annual typical scene set are divided into summer, transition season and winter, wherein the summer is the 152th to 243th day of the year, the transition season is the 60th to 151th day and the 244th to 334th day of the year, and the winter is the 1st to 59th day and the 335th to 365th day of the year.

[0033] According to some embodiments of the present application, the annual disordered boundary sequence is determined according to the set of annual typical scene times and the first probability set, and the annual scene boundary with time sequence characteristics is generated by optimizing the annual disordered boundary sequence according to the genetic algorithm based on the time sequence of the representative meteorological year data set.

[0034] According to the annual typical scenario time set and the first probability set, a corresponding first probability of each typical scenario time is determined respectively;

[0035] Each first probability is multiplied by a corresponding seasonal day number to obtain a corresponding frequency of occurrence of the typical scenario time in a corresponding hour;

[0036] According to the frequency, each typical scenario time is repeatedly arranged and combined to form an unordered boundary sequence corresponding to the summer day number*24 hours, the transition season day number*24 hours and the winter day number*24 hours, and all unordered boundary sequences are combined into the annual unordered boundary sequence;

[0037] Taking the time sequence of the representative meteorological year data set as a reference, and dividing the representative meteorological year data set according to the same seasonal day number, the annual unordered boundary sequence corresponding to each season is sorted and optimized according to a genetic algorithm to generate the annual scenario boundary with time sequence characteristics.

[0038] In a second aspect, the technical solution of the present application also relates to a planning method based on source-load matching and time sequence characteristics, which further includes the following steps:

[0039] The annual scenario boundary in the above-mentioned source-load matching and time sequence characteristic-based uncertainty scenario generation method is input as a system operation boundary, and the system capacity configuration is optimized and solved based on a multi-objective optimization algorithm to determine the capacity random planning variable set of the system.

[0040] The present application has the following advantages:

[0041] By dividing the sample points in each hour according to the source-load difference, the characteristics of positive / negative differences in source-load matching can be distinguished, and by reducing the scenario time in each hour respectively, the characteristics of source-load matching can be effectively retained to generate more accurate typical scenarios, improve the accuracy of system reliability evaluation, and optimize the time sequence characteristics of historical data to finally generate an annual scenario boundary with time sequence characteristics. The present application combines the time sequence correlation and operation coupling characteristics of the source and load and between them, can effectively improve the rationality of the annual scenario boundary, significantly reduce the subsequent operation performance deviation, effectively optimize the uncertainty boundary, improve the rationality, and thus obtain more optimal uncertainty scenario and capacity random planning variable set results, effectively reduce the operation performance deviation, and effectively improve the engineering implementability of the random planning scheme. BRIEF DESCRIPTION OF DRAWINGS

[0042] Figure 1 The step flowchart of the source-load matching and time sequence characteristic-based uncertainty scenario generation method of the present application;

[0043] Figure 2 Step flow chart for reference year generation method of the present application;

[0044] Figure 3 Step flow chart for typical year construction method of the present application;

[0045] Figure 4 Step flow chart for conventional uncertain boundary generation;

[0046] Figure 5 Step flow chart for determining the set of initial hourly wind and solar load scenarios of the present application;

[0047] Figure 6 Step flow chart for determining the set of initial hourly wind and solar load scenarios and the set of reference capacity optimization variables of the present application;

[0048] Figure 7 Step flow chart for the present application to reduce the set of hourly scenarios corresponding to solar irradiance in the set of initial hourly wind and solar load scenarios;

[0049] Figure 8 Step flow chart for the present application to reduce the set of initial hourly wind and solar load scenarios of the present application;

[0050] Figure 9 Effect impact display chart for the present application K-means clustering algorithm to source load matching features and time sequence features;

[0051] Figure 10 Step flow chart for the present application to determine the annual scenario boundary;

[0052] Figure 11 Source load difference DE curve comparison chart of the present application;

[0053] Figure 12 Step flow chart for the planning method of the present application based on source load matching and time sequence features. DETAILED DESCRIPTION

[0054] The concept, specific structure and generated technical effects of the present application will be described clearly and completely in combination with embodiments and drawings below, so as to fully understand the purpose, scheme and effect of the present application.

[0055] It should be noted that, unless otherwise specified, when a certain feature is referred to as being "fixed", "connected" to another feature, it can be directly fixed, connected to the other feature, or indirectly fixed, connected to the other feature. It should be noted that, unless otherwise specified, when a certain feature is referred to as being "electrically connected" or "electrically connected" to another feature, the two features can be directly connected through a pin, or connected through a cable, or connected through a wireless transmission mode. The specific electrically connected mode belongs to the general mode of those skilled in the art, and those skilled in the art can realize the connection according to the needs. The singular forms "a", "said" and "the" used in this paper are also intended to include the plural forms, unless the context clearly indicates otherwise. In addition, unless otherwise defined, all technical and scientific terms used in this paper have the same meaning as understood by those skilled in the art. The terms used in the specification of this paper are only used to describe specific embodiments, and are not intended to limit the present application. The term "and / or" used in this paper includes any combination of one or more related listed items.

[0056] It should be understood that although the terms first, second, third, etc. can be used in this disclosure to describe various elements, these elements should not be limited to these terms. These terms are only used to distinguish one type of element from another. For example, without departing from the scope of the disclosure, the first element can also be referred to as the second element, and similarly, the second element can also be referred to as the first element. The use of any and all examples or exemplary language ("for example", "for example", etc.) provided herein is only intended to better illustrate the embodiments of the present application, and unless otherwise required, does not impose any limitation on the scope of the present application.

[0057] In a first aspect, with reference to Figure 1 The technical solution of the present application relates to a method for generating an uncertainty scenario based on source-load matching and timing characteristics, comprising the following steps:

[0058] Step S100, according to the historical data of the target area in previous years, a representative meteorological year data set, a reference capacity optimization variable set and a full-year wind-light-load initial scenario time set are determined respectively;

[0059] Step S200, based on the reference capacity optimization variable set, the source-load difference value of the wind-light-load sample point of each hour of the full-year wind-light-load initial scenario time set is calculated DE According to the positive and negative of the corresponding source-load difference value DE , the wind-light-load sample points in each hour are divided into a surplus power state region set where the source is greater than the load and a power shortage state region set where the source is less than the load;

[0060] Step S300, based on the distance clustering algorithm, the corresponding residual power state region set and the power shortage state region set of the wind and light load sample points are clustered respectively, and after iteration convergence, the annual typical scene time set and the corresponding first probability set are determined;

[0061] Step S400, according to the annual typical scene time set and the first probability set, the annual disordered boundary sequence is determined, and according to the genetic algorithm, the annual disordered boundary sequence is optimized based on the time sequence of the representative meteorological year data set, and the annual scene boundary with time sequence characteristics is generated.

[0062] In step S100, the historical data of the target area in previous years at least includes wind speed, solar radiation, power load and other conventional climate parameters in the corresponding year, and in this embodiment, the initial scene time set of the annual wind and light load is the set of the initial scene time set of the wind and light load corresponding to 24 hours in summer, 24 hours in transition season and 24 hours in winter. In this embodiment, the summer is the 152th to 243rd day of the year, the transition season is the 60th to 151st day and the 244th to 334th day of the year, and the winter is the 1st to 59th day and the 335th to 365th day of the year. It should be noted that the representative meteorological year data set, the reference capacity optimization variable set and the initial scene time set of the annual wind and light load can be determined by analyzing the historical data of the target area in previous years by means commonly used by those skilled in the art.

[0063] Step S200 in the application divides the sample points in each hour by the source-load difference, which can distinguish the characteristics of positive / negative differences in source-load matching, and in step S300, the scene time in each hour is reduced, which can effectively retain the characteristics of source-load matching, generate more accurate typical scenes and improve the accuracy of system reliability evaluation. Step S400 can retain the time sequence characteristics of historical data and generate an annual scene boundary with time sequence characteristics. That is, the application combines the time sequence correlation and operation coupling characteristics of the source, the load and the relationship between them, which can effectively improve the rationality of the annual scene boundary, significantly reduce the subsequent operation performance deviation, effectively optimize the uncertain boundary, improve the rationality, and further obtain better uncertainty scenes and planning results, effectively reduce the operation performance deviation, and effectively improve the engineering implementability of the random planning scheme.

[0064] It can be known that in step S100, the representative meteorological year data set can be determined by the reference year generation method or the typical year construction method based on the historical data in previous years.

[0065] Reference Figure 2 In some embodiments of the application, the reference year generation method includes the following steps:

[0066] Step S111, according to the historical data of at least 5 consecutive years of the target area, determine the average wind speed, annual total radiation and total load of each year;

[0067] Step S112, based on the parameters of the average wind speed, annual total radiation and total load of at least 5 consecutive years of the target area, sort the corresponding parameters of each year respectively, and determine the ranking of the corresponding parameters of each year;

[0068] Step S113, based on the ranking of the corresponding parameters of each year, determine the comprehensive median deviation of each year D ;

[0069] Step S114, based on the comprehensive median deviation of each year D， Select the deviation D The smallest year of historical data as a representative meteorological year data set; when there are multiple years of deviation D The same and the minimum case, according to the preset parameter priority order, select the year with the more central annual ranking as the representative meteorological year data set, and the parameter priority order is the average wind speed, annual total radiation and total load.

[0070] In combination with steps S111 to S114, at least 5 years of historical data of the target area are selected, and the average wind speed, annual total radiation and total load corresponding to each year are calculated respectively, and the selected years are sorted independently according to the corresponding variables of the average wind speed, annual total radiation and total load, and the comprehensive median deviation of the average wind speed, annual total radiation and total load corresponding to each year is calculated respectively D The formula for calculating the deviation D is as follows:

[0071] (1);

[0072] In the formula, R WS , R DNI and R Load are the rankings of the average wind speed, annual total radiation and total load of the corresponding year, M is the median value, if the deviation is the same, the year with the more central wind speed ranking (the time series fluctuation of the wind speed is larger) is selected first, if the wind speed ranking is still the same, the year with the more central annual total radiation is selected; if the ranking of the annual total radiation is also the same, the year with the more central total load is selected, and finally the same, a year of data is randomly selected as the representative meteorological year data set.

[0073] It should be noted that in steps S111 to S114, the skilled person in the art can select the variables to be referred to from the historical data according to the actual situation, and it is not necessary to limit the average wind speed, the total annual radiation and the total load, and corresponding adjustment can be made in combination with the demand of energy planning.

[0074] With reference to Figure 3 In some embodiments of the present application, the typical annual construction method comprises the following steps:

[0075] Step S121, pre-processing the historical data of at least 10 consecutive years of the target area to determine the daily average of each month of each year of the first target parameter, the first target parameter including solar radiation, wind speed, dry bulb temperature and humidity;

[0076] Step S122, determining the long-term average cumulative distribution function of each year and each month and the single-year cumulative distribution function of each month according to the daily average of each first target parameter;

[0077] Step S123, selecting the month with the smallest mean square error of the corresponding month as the target month based on the mean square error of the single-year cumulative distribution function and the long-term average cumulative distribution function of each year and each month, and based on the months from January to December;

[0078] Step S124, splicing and smoothing the hour data of the target months corresponding to the months from January to December in order to serve as a representative meteorological year data set.

[0079] In the execution of steps S121 and S122, the long-term average cumulative distribution function is:

[0080] (2);

[0081] For each month m , all years are arranged in ascending order according to the corresponding daily average data, and the long-term average cumulative distribution function is calculated according to formula (2) C long ( p, m, i ), in which formula (2): R ( i ) represents the order of the i-th daily average in all years of the month, N is the total number of days in the month in all years, m is the corresponding value of the months from January to December;

[0082] The single-year cumulative distribution function of each month is:

[0083] (3);

[0084] The first mArrange the daily averages for each month in ascending order, and calculate the annual cumulative distribution function for each month according to formula (3). C single ( p, y, m, i In the formula: r ( i ) indicates the year's number m Mid-month i Sorting of daily averages n This represents the number of days in the current month.

[0085] Then, steps S123 and S124 are executed to select the month with the smallest mean square error as the target month, and time smoothing is performed on the hourly data at the splicing points between the target months to ensure the climate continuity when spliced ​​into a complete year. The resulting typical meteorological year dataset retains key climate characteristics, including the average value, frequency distribution, correlation between parameters, and temporal consistency of the entire annual cycle. Finally, the typical meteorological year dataset is combined into a representative meteorological year dataset.

[0086] Reference Figure 4 The conventional steps for generating uncertain boundaries mainly include four steps: probability distribution fitting, sampling, clustering, and boundary construction. Therefore, when determining the set of reference capacity optimization variables, this invention can use conventional uncertain boundaries as the operating boundaries of the system and optimize them through a multi-objective optimization algorithm to determine the corresponding parameters.

[0087] Reference Figure 5 In some embodiments of the present invention, determining the initial scene set of wind, solar and monsoon throughout the year includes the following steps:

[0088] Step S131: Based on historical data of at least 5 consecutive years for the target area, the second target parameter within each hour is fitted using the Gaussian kernel density estimation method to determine the probability density function corresponding to the second target parameter for each hour, and the cumulative distribution function corresponding to the second target parameter for each hour is obtained by integration. The second target parameters include wind power, solar irradiance and power load.

[0089] Step S132: Based on the cumulative distribution function corresponding to each hour, construct the three-dimensional joint cumulative distribution function of wind, solar and load corresponding to the second objective parameter of each hour in a hierarchical manner using the Frank-Copula function;

[0090] Step S133: Based on the three-dimensional joint cumulative distribution function of wind and solar load per hour, use Monte Carlo or Latin hypercube sampling methods to generate joint sampling points for each hour, and perform inverse transformation on the corresponding joint sampling points for each hour to determine the sample values ​​corresponding to each joint sampling point within each hour. The sample values ​​include the first sample value corresponding to wind power, the second sample value corresponding to solar irradiance, and the third sample value corresponding to power load. The first sample value, the second sample value, and the third sample value corresponding to each hour are used as the wind and solar load sample points for each hour to form the initial scenario of wind and solar load for each hour.

[0091] Step S134: Combine the initial scene time of each hour of the wind and light lotus by day to form the initial full-day scene of the wind and light lotus, and divide it into summer, transition season and winter, and generate summer scene respectively. 24 Hours, transitional season 24 Hours and winter 24 The initial scene time set corresponding to each hour is used to combine all the initial scene time sets of the wind, light and lotus to form the initial scene time set of the wind, light and lotus for the whole year.

[0092] In step S131, the formula for the probability density function is:

[0093] (4);

[0094] Formula (4) represents the point x The Gaussian kernel probability density function at point is given by the formula. n For the sample size, h For bandwidth, x i For the first one drawn from the set i There are 10 independent samples; the bandwidth can be determined using the thumb method, and the optimal bandwidth is:

[0095] (5);

[0096] In formula (5) σ The standard deviation of the data set corresponding to the observed samples. n The number of samples;

[0097] In step S132, the formula for the three-dimensional joint cumulative distribution function of wind, solar, and load is:

[0098] (6);

[0099] Among them, in formula (6) α 1 , α 2 They are respectively F 1 ,F 2 and C 12 with F 3 between the Frank-Copula parameters, the formula is to use Copula function to establish joint distribution function, by two levels of Frank-Copula function to describe the dependence structure between wind and light load.

[0100] In the execution step 131, based on the target area at least 5 years of historical data, the same annual summer, transition season and winter three seasons corresponding to the number of days, in each season inside the Gaussian kernel density estimation method is respectively used to each hour corresponding to the wind power, solar radiation and power load and other parameters are respectively probability distribution modeling, that is, according to formula (4) and formula (5), respectively, the probability density function corresponding to each parameter f 1 , f 2 and f 3 , the probability density function f 1 corresponding to the wind power parameters, the probability density function f 2 corresponding to the solar radiation parameters, the probability density function f 3 corresponding to the power load parameters; and respectively on the probability density function f 1 , f 2 and f 3 integral, the corresponding cumulative distribution function F 1 , F 2 and F 3 , and respectively corresponding to formula (6) F 1 , F 2 and F 3 , then step S132 is executed, that is, according to formula (6) for calculation, then can obtain each hour of joint cumulative distribution function.

[0101] In the execution step S133, first using Monte Carlo or Latin hypercube combined with Copula joint sampling method, the joint distribution function obtained in step S132 is sampled, a large number of hours joint sampling points are generated, and the sample value corresponding to the cumulative probability is calculated by means of cubic spline interpolation method, that is, inverse transformation, that is, the first sample valuex 1 , the second sample value corresponding to the solar irradiance x 2 , and the third sample value corresponding to the power load x 3 Specifically, the sample value corresponding to the cumulative probability can be approximately calculated by means of the cubic spline interpolation method, and the specific calculation formula is:

[0102] (7);

[0103] Wherein, the u 1 , u 2 and u 3 in formula (7) correspond to the cumulative probability values of the wind power, the solar irradiance and the power load respectively, which are generated by formula (6) combined with the Monte Carlo or Latin hypercube sampling technology; a 1 , a 2 , a 3 , b 1 , b 2 , b 3 , c 1 , c 2 , c 3 , d 1 , d 2 and d 3 are the coefficients of the cubic spline interpolation respectively, which can be calculated in advance by the cubic spline interpolation method; F 1 -1 , F 2 -1 and F 3 -1 are the inverse functions corresponding to the cumulative distribution functions F 1 , F 2 and F 3 respectively;

[0104] Then the first sample value x 1, the second sample value x 2 and the third sample value x 3 As the wind and light load sample point corresponding to each hour, to constitute the initial scene of wind and light load of each hour;

[0105] Finally, according to step S134, combination and division can be performed to correspond to the initial scene of wind and light load of the whole day and the initial scene of wind and light load of the whole year.

[0106] Referring to Figure 6 In some embodiments of the present application, the determination of the reference capacity optimization variable set comprises the following steps:

[0107] Step S135, based on the distance clustering algorithm, all the initial scenes of wind and light load of the whole day are clustered day by day, and after iterative convergence, the set of typical scene days of the whole year and the corresponding second probability set are determined;

[0108] Step S136, according to the index performance of each typical scene day in the set of typical scene days of the whole year, and weighted sum according to the corresponding second probability set, the index performance of the whole year is determined C PWAB ;

[0109] Step S137, the index performance C PWAB is input as the system operation boundary, and the reference capacity configuration is optimized and solved based on the multi-objective optimization algorithm to determine the reference capacity optimization variable set.

[0110] Wherein, after step S134 is executed, step S135 is continued to be executed, in step S135, the distance clustering algorithm can be a conventional distance clustering algorithm such as K-means clustering method or hierarchical clustering method, the set of initial scenes of wind and light load in step S134 can be reduced according to the needs of the field to obtain the typical scene days and the corresponding second probability, and all the typical scene days and the corresponding second probability are combined to constitute the set of typical scene days of the whole year and the corresponding second probability set.

[0111] The present application adopts the K-means clustering method in the reduction mode, and reduces the set of initial scenes of wind and light load in units of "day", and the reference Figure 7 , Figure 7 is the solar radiation determined after steps 133 to 135 DNI corresponding to the step flow effect diagram of the whole day scene reduction.

[0112] Wherein, the index performance C PWAB The formula is:

[0113] (8);

[0114] formula (8) is C ( t, j ) is the corresponding index performance of the i-th typical scenario in the j-th hour; t j P j is the probability of the i-th typical scenario occurring. j

[0115] After determining the corresponding index performance C PWAB , step S137 is performed, and when evaluating the system operation performance, in this embodiment, the net present value, the wind and light curtailment rate, and the power following deviation are taken as evaluation indexes to confirm the reference capacity optimization variable set, and the specific steps are as follows:

[0116] The index performance is taken as the system operation boundary input, a multi-objective optimization algorithm is used to optimize and solve the system reference capacity configuration, and the ideal point is formed by the optimal values of each single objective in the Pareto solution set, and the decision variable combination corresponding to the solution with the minimum Euclidean distance from the point is taken as the reference capacity optimization variable set; wherein, the multi-objective optimization algorithm can be an NSGA-II algorithm or a particle swarm algorithm based on the Pareto frontier to optimize the capacity, in this embodiment, the NSGA-II algorithm based on the Pareto frontier is used, the maximum net present value, the minimum wind and light curtailment rate, and the minimum power following deviation are taken as the targets, and finally the ideal point is formed by the optimal values of each single objective in the Pareto solution set, and the decision variable combination corresponding to the solution with the minimum Euclidean distance from the point is taken as the reference capacity optimization variable set, in this embodiment, the reference capacity optimization variable set includes:

[0117] reference wind turbine number N w1 , unit: [unit];

[0118] reference photovoltaic installed capacity C pv1 , unit: [MW];

[0119] reference solar thermal installed capacity C csp1 , unit: [MW];

[0120] reference heat storage duration TES 1 , unit: [h];

[0121] reference mirror field magnification SM 1 . ​​​

[0122] In some embodiments of the present application, the determination of the reference capacity optimization variable set comprises the following steps:

[0123] Step S141, taking the data of the representative meteorological year data set as the system operation boundary, and based on a multi-objective optimization algorithm, optimizing and solving the system capacity configuration to determine the reference capacity optimization variable set.

[0124] It can be known that in step S141, the multi-objective optimization algorithm used can also be the NSGA-II algorithm or the particle swarm algorithm based on the Pareto frontier for capacity optimization, and the specific calculation method belongs to the routine technical means of those skilled in the art. Therefore, reference can be made to step 137 above to determine the parameters of the reference capacity optimization variable set, which will not be described in detail here.

[0125] It can be known that in the embodiments of the present application, the reference capacity optimization variable set can be calculated according to step S141 or step S137, and those skilled in the art can select appropriate calculations to determine according to the situation.

[0126] In some embodiments of the present application, step S200 calculates the source-load difference value DE The formula is:

[0127] (9);

[0128] Wherein, the source-load difference value DE The unit is [MW];

[0129] N w1 And C pv1 Then the reference wind power installed capacity N w1 And the reference photovoltaic installed capacity C pv1 Solved by step S141 or step S137 respectively.

[0130] W p is the sample point corresponding to the wind power in each hour, with the unit of [MW];

[0131] Load is the sample point corresponding to the power load in each hour, with the unit of [MW];

[0132] The numerical value 5.56 is the component area required for each kilowatt of photovoltaic installed capacity, with the unit of [m 2 ];

[0133] When the source-load difference value DEIf > 0, the corresponding wind and light load sample point is collected in the surplus power state region set;

[0134] When the source load difference value DE < 0, the corresponding wind and light load sample point is collected in the power shortage state region set.

[0135] Step S200 calculates the sample points in each hour according to formula (9), so that different scene sample points in each hour can be divided into two different regions for processing according to the source load matching state in each hour, which can avoid the subsequent K-means clustering from dividing sample points with different states into the same category, neutralize the source load matching characteristics of the hour sample points, and retain the source load matching characteristics, so as to more accurately capture the volatility of renewable energy and load, and improve the accuracy of system reliability evaluation.

[0136] In some embodiments of the application, the distance clustering algorithm in step S300 can also be a conventional distance clustering algorithm such as the K-means clustering method or the hierarchical clustering method, and a suitable algorithm can be selected according to the needs in the art to reduce the initial scene time set of wind, light and load throughout the year to obtain the typical scene time and the corresponding first probability, which can constitute the annual typical scene time set and the corresponding first probability set. In the present embodiment, the K-means clustering method is used in step S300 to reduce the initial scene time set of wind, light and load throughout the year by taking hours as the target.

[0137] The core idea of the K-means algorithm is to divide the samples into several clusters and use the center of each cluster to represent the characteristic distribution of the cluster, and in the process of assigning samples to clusters, each sample is assigned to the nearest cluster center.

[0138] Taking a certain uncertain parameter sampling data set X in a certain hour as an example (the same processing method is used for the other two dimensions of the uncertain parameter), assuming that the data X=[ x 1 , x 2 ,..., x n ], randomly select k samples from the data as the initial clustering centers v [ v 1 , v 2 ,..., v k ]. Calculate the distance of the data x i to each clustering center:

[0139] (10);

[0140] Based on the distance of each sample to the cluster center, data points are classified and assigned to the cluster corresponding to the nearest cluster center. Then, the mean of the data within each cluster is calculated and used as the new cluster center.

[0141] (11);

[0142] In the formula: N j Indicates the first j The number of samples contained within a cluster.

[0143] Repeat the above-described steps of classifying and updating cluster centers until the cluster centers no longer change or the preset maximum number of iterations is reached, at which point the algorithm terminates. k The cluster centers represent the typical scenarios obtained through reduction. The probability corresponding to each typical scenario is calculated by the following formula:

[0144] (12).

[0145] Based on step S300, the wind and solar load sample points of the corresponding surplus power state area set and power shortage state area set are clustered hourly. After iterative convergence, the typical scenario time and their corresponding probability are obtained for each hour. This step does not involve the temporal characteristics of the data, that is, the fluctuation trend of the combination between hours. It can independently handle the impact of clustering on source-load matching characteristics.

[0146] Step S300, based on the core idea of ​​the K-means algorithm, clusters the sample data of the surplus power state region set and the power shortage state region set for each hour. After iterative convergence, the typical scenario time and its corresponding first probability are obtained. Combining all the typical scenario times and their corresponding first probabilities determines the set of typical scenario times and their corresponding first probabilities for the entire year. It is important to note that the distance clustering algorithm in step S300 clusters on an hourly basis. (Refer to [reference needed]). Figure 8 This is a rendering of the scene reduction in step S300.

[0147] Similarly, it can be seen that step S135 uses the same algorithm, but unlike step S300, step S135 performs clustering on a "day" basis. For ease of subsequent description, it can be understood that step S300 implements a "point clustering" method, while step S135 performs a "line clustering" method.

[0148] Specifically, after steps S200 and S300, a comparison is made. Figure 7 , Figure 8 andFigure 9 As can be seen, the present invention can effectively preserve the source-load matching difference characteristics, without involving time series fluctuations, and can provide a clear and typical data foundation for the time series analysis and optimization of step S400.

[0149] The initial scene set for the year-round wind, solar and lotus in step S100 includes 24 hours of wind, solar and lotus data for three seasons, namely summer. 24 Hours, transitional season 24 Hours and winter 24 The initial scene time set of the wind and light lotus for each hour, after executing steps S200 and S300 respectively on the initial scene time set of the wind and light lotus for the whole year, can yield a size of k×24×3 The typical scenario time set throughout the year and the corresponding first probability set.

[0150] Reference Figure 10 In some embodiments of the present invention, step S400 includes:

[0151] Step S410: Based on the set of typical scenarios throughout the year and the set of first probabilities, determine the first probability corresponding to each typical scenario.

[0152] Step S420: Multiply each first probability by the corresponding number of days in the season to obtain the frequency of occurrence of the corresponding typical scenario within the corresponding hour;

[0153] Step S430: Based on frequency, repeatedly arrange and combine each typical scenario to form the number of summer days. ×24 Hours, number of days in the transition season ×24 Hours and winter days ×24 The disordered boundary sequence corresponding to each hour is combined into a whole year disordered boundary sequence.

[0154] Step S440: Based on the time series of the representative meteorological year data set, the representative meteorological year data set is divided according to the same number of days in each season. The unordered boundary sequence of the whole year corresponding to each season is sorted and optimized according to the genetic algorithm to generate the whole year scene boundary with time series characteristics.

[0155] The formula for frequency calculation in step S420 is as follows:

[0156] (13);

[0157] In formula (13): N f,j For the first j The frequency of occurrence of a typical scenario within a certain hour; P j For the firstj the probability corresponding to the typical scenario in the typical scenario; Day season For the number of days corresponding to the season, in this embodiment, the whole year is divided into summer, transition season and winter, the summer is the 152th~243th day of the whole year, the transition season is the 60th~151th day and the 244th~334th day of the whole year, and the winter is the 1st~59th day and the 335th~365th day of the whole year;

[0158] After determining the frequency of each hour, step S430 is performed to arrange and combine the corresponding typical scenario time according to the frequency to form a complete sequence of each hour in the season, each hour is arranged and combined according to the same operation, and then the original disordered boundary sequence of each hour corresponding to the size of Day season ×24 The original disordered boundary sequences of the three seasons are combined to form a whole year disordered boundary sequence.

[0159] The genetic algorithm model formula of step S440 is as follows:

[0160] (14);

[0161] In formula (14), F ED represents the fitness function, D(X,Y) is a standard Euclidean distance function, D WP (X,Y), D DNI (X,Y), D Load (X,Y), D DE (X,Y) respectively correspond to the wind and light load and D DE Source load difference value DE The weighted Euclidean distance between the reordered sequence and the reference sequence is composed of the reordered sequence based on the original disordered boundary sequence, and the reference sequence is the data vector sequence corresponding to the representative meteorological year data set in step S100. The representative meteorological year data is also divided according to the same number of days in the season, and the corresponding data vector is taken as the reference sequence; X, Y The corresponding data vector of the reordered sequence and the reference sequence is respectively X The reordered sequence can be determined from the corresponding data vector of the whole year disordered boundary sequence. It should be noted that the parameters corresponding to the reference sequence and the matched reordered sequence should be of the same type of parameter type; 、 、 、 are weight coefficients, here, since the fluctuation of wind and light is larger, higher decision weight is given, and in actual application, it can be adjusted according to actual demand.

[0162] Therefore, combined with formula (14), the optimization of the whole-year disordered boundary sequence can be completed to obtain the ordered sequence of the corresponding season Day season ×24 ] opt ; then, the seasonal day number order is combined to form the whole-year scene boundary 365×24 Compared with the traditional uncertain boundary, the whole-year scene boundary retains better source-load matching characteristics and time sequence characteristics, and the whole-year scene boundary formula is as follows:

[0163] (15).

[0164] Referring to Figure 11 , the reference year DE curve in the figure is a kind of representative meteorological year data set, it can be seen that the load-source difference DE curve corresponding to the summer of the present application has the same time sequence characteristics as the reference sequence after the time sequence optimization by the genetic algorithm, therefore, the curves of other parameters will also have the same characteristics after optimization, this time, specific display is not performed, finally, the whole-year scene boundary has the corresponding time sequence characteristics.

[0165] Therefore, compared with the conventional generated uncertain boundary, the whole-year scene boundary obtained by the steps S100 to S400 of the present application can retain the source-load matching characteristics and time sequence characteristics, and is more reasonable, which can effectively reduce the error of system operation.

[0166] Secondly, referring to Figure 10 , the technical scheme of the present application also relates to a planning method based on source-load matching and time sequence characteristics, and further includes the following steps:

[0167] Step S500, input the whole-year scene boundary in the above-mentioned uncertain scene generation method based on source-load matching and time sequence characteristics as the system operation boundary, and based on the multi-objective optimization algorithm, optimize and solve the system capacity configuration to determine the capacity random planning variable set of the system.

[0168] Specifically, during optimization, the maximum system net present value, the minimum wind and light curtailment rate, and the minimum power following deviation are taken as optimization objectives, and a multi-objective optimization algorithm is used for optimization solution to obtain the capacity stochastic programming variable set. Similarly, the annual scenario boundary in the above step S400 can be taken as the system operation boundary input, a multi-objective optimization algorithm is used for optimization solution of the system capacity configuration, and the optimal values of each single objective in the Pareto solution set form an "ideal point", and the decision variable combination corresponding to the solution with the minimum Euclidean distance from the point is taken as the capacity stochastic programming variable set. In this embodiment, the NSGA-II algorithm based on the Pareto frontier is also used, the maximum net present value, the minimum wind and light curtailment rate, and the minimum power following deviation are taken as the objectives, and finally the optimal values of each single objective in the Pareto solution set form an "ideal point", and the decision variable combination corresponding to the solution with the minimum Euclidean distance from the point is taken as the capacity stochastic programming variable set. In this embodiment, the capacity stochastic programming variable set includes:

[0169] Number of wind power installations N w2 , unit: [unit];

[0170] Photovoltaic installation capacity C pv2 , unit: [MW];

[0171] Thermal power installation capacity C csp2 , unit: [MW];

[0172] Heat storage duration TES 2 , unit: [h];

[0173] Mirror field magnification SM 2 .

[0174] Meanwhile, it is worth noting that the reference capacity optimization variable set determined through step S100 can be analyzed and determined through step S141 or step S137 to determine the reference capacity optimization variable set. The detailed steps corresponding to the determination of the reference capacity optimization variable set are the technical means for determining the capacity stochastic programming variable set in the industry, but in this application, the decision variable set obtained by the conventional technical means is taken as the reference variable. On this basis, the reference capacity optimization variable set is taken as the reference value during the source-load matching of step S200, so as to confirm a more reasonable capacity stochastic programming variable set, which can effectively reduce the operation performance deviation and effectively improve the engineering implementability of the stochastic programming scheme.

[0175] Based on the above, the following is based on source load matching and timing characteristics of uncertainty scenario generation and planning method, combined with actual data for simulation analysis, as follows:

[0176] The embodiment adopts the meteorological data (including wind speed, radiation, temperature and other parameters) of 8760h x 10 years in China Xilingol region (43°93'N, 116°05'E) from 2009 to 2018, and generates 8760h wind light load data and conventional uncertain boundary of a typical year. First, the deterministic capacity optimization is carried out under the boundary of a typical year, and the optimal decision planning obtained is the deterministic capacity optimization result:

[0177] The wind power installed capacity is 75 units (112.5 MW), the photovoltaic installed capacity is 92 MW, the photothermal installed capacity is 92 MW, the heat storage time is 16 hours, and the mirror field ratio is 1.823;

[0178] The deterministic capacity optimization result is used as a reference capacity optimization variable set, and the annual scene boundary generated based on steps S100 to S400 of the present application is used as an improved uncertain boundary.

[0179] On this basis, combined with the scheme of the present application:

[0180] 1. The first uncertain boundary obtained by combining only steps S200 and S300 is obtained, that is, only the source load matching and "point clustering" are considered, and at step S400, the timing optimization is not considered, so that the corresponding first uncertain boundary is obtained;

[0181] 2. Without considering the source load matching problem, the corresponding typical scene time set and probability set are obtained by directly performing "line clustering" in the conventional manner, that is, in "day" units, and then only combining the timing optimization of step S400, the corresponding uncertain boundary is obtained as the second uncertain boundary.

[0182] In order to highlight the rationality and effectiveness of the annual scene boundary, that is, the uncertain boundary generation scheme of the present application, under the same decision planning scheme [75, 92, 92, 16, 1.823], the above-mentioned corresponding system running index performance is compared with the average performance of the index under the actual boundary from 2019 to 2023, and the results are shown in Table 1:

[0183]

[0184] From the comparison results in Table 1, it can be seen that the relative error of the uncertain boundary generated by the conventional random planning scheme to the actual mean value is more than 10% in the performance of the three system operation indicators, and the relative error of the wind and light rejection rate is even as high as 99.03%, which indicates that the conventional uncertain boundary is unreasonable, resulting in serious operation performance deviation of the system. Meanwhile, referring to the first uncertain boundary and the second uncertain boundary, the corresponding error is also high, which indicates that considering the source-load matching characteristics, "point clustering" or time sequence characteristics alone cannot completely solve the problems in the conventional scheme, and the three need to be combined simultaneously, that is, the steps S200 of distinguishing the source-load matching, the step S300 of reducing the scene in the manner of "point clustering" and the step S400 of optimizing in combination with the time sequence characteristics can achieve the optimal effect. The net present value, the wind and light rejection rate and the power following deviation of the scheme of the present application to the actual mean value are 1.65%, 6.15% and 0.86%, which are reduced by 13.76%, 99.28% and 13.22% compared with the conventional scheme, fully indicating that the present application is more reasonable and can effectively reduce the operation performance deviation.

[0185] The uncertainty capacity planning scheme determined based on the steps S100 to S500 of the present application, that is, the capacity random planning variable set is:

[0186] There are 73 wind power units (109.5 MW), 101 MW of photovoltaic units, 100 MW of photo-thermal units, 16 hours of heat storage time and 1.499 of mirror field magnification;

[0187] The system indicators under different boundaries are compared under the capacity random planning variable set, and the results are shown in Table 2:

[0188]

[0189] From Table 2, it can be seen that under the planning method of the present application based on the source-load matching and time sequence characteristics, the indicator performance deviation of the improved uncertain boundary to the actual mean value is still within 10%, which again fully indicates the rationality of the method of the present application.

[0190] According to the embodiments of the present application, some effects can be achieved as follows: by dividing the sample points in each hour by the source-load difference value, the characteristics of positive / negative differences of source-load matching can be distinguished, and by reducing the scene in each hour respectively, the characteristics of source-load matching can be effectively retained to generate more accurate typical scenes, the accuracy of system reliability evaluation is improved, and the historical data is optimized by using the time sequence characteristics, and finally the annual scene boundary with time sequence characteristics can be generated, the source, load and the time sequence correlation and operation coupling characteristics between the source and load are combined, the rationality of the annual scene boundary is effectively improved, the subsequent operation performance deviation is significantly reduced, the uncertain boundary is effectively optimized, the rationality is improved, and then the results of the optimal uncertainty scene and capacity random planning variable set can be obtained, the operation performance deviation is effectively reduced, and the engineering implementability of the random planning scheme is effectively improved.

[0191] It should be appreciated that the method steps in the embodiments of the present application can be realized or implemented by computer hardware, a combination of hardware and software, or through computer instructions stored in a non-transitory computer readable memory. The uncertainty scene generation method based on source-load matching and time sequence characteristics can use standard programming techniques. Each program can be implemented in a high-level procedural or object-oriented programming language to communicate with a computer system. However, if necessary, the program can be implemented in assembly or machine language. In any case, the language can be a compiled or interpreted language. In addition, the program can be run on a programmed special-purpose integrated circuit for this purpose.

[0192] In addition, the operations of the processes described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The processes described herein (or variations and / or combinations thereof) can be performed under the control of one or more computer systems configured with executable instructions (e.g., executable instructions, one or more computer programs or one or more applications), hardware or combinations thereof configured to perform the operations described herein. The computer programs include a plurality of instructions executable by one or more processors.

[0193] Further, the methods can be implemented in any type of computing platform operably connected to a suitable computing platform, including but not limited to a personal computer, mini-computer, mainframe, workstation, network or distributed computing environment, separate or integrated computer platforms, or in communication with charged particle tools or other imaging devices, and the like. Aspects of the present application can be implemented in machine readable code stored on a non-transitory storage medium or device, whether removable or integrated into a computing platform, such as a hard disk, optical read and / or write storage media, RAM, ROM, and the like, such that it can be read by a programmable computer to configure and operate the computer to perform the processes described herein when the storage medium or device is read by the computer. In addition, the machine readable code, or portions thereof, can be transmitted over wired or wireless networks. The present application described herein includes these and other different types of non-transitory computer readable storage media when such media include instructions or programs that implement the steps described above in conjunction with a microprocessor or other data processor. The present application can also include the computer itself when programmed in accordance with the methods and techniques described herein.

[0194] The computer program can be applied to input data to perform the functions described herein to transform the input data to generate output data that is stored to non-volatile memory. The output information can also be applied to one or more output devices such as a display. In a preferred embodiment of the present application, the transformed data represents a physical and tangible object, including a particular visual depiction of the physical and tangible object produced on a display.

[0195] The above description is only preferred embodiments of the present application, and the present application is not limited to the above-described embodiments, but any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the present application. The technical solutions and / or embodiments of the present application can have various modifications and changes within the scope of the present application.

Claims

1. A method for generating uncertainty scenarios based on source load matching and timing characteristics, characterized in that, The method comprises the following steps: According to the historical data of the target area in previous years, a representative meteorological year data set, a reference capacity optimization variable set, and a full-year wind and light load initial scenario time set are determined respectively; Based on the reference capacity optimization variable set, the source-load difference value of the wind and light load sample point of each hour of the full-year wind and light load initial scenario time set is calculated, and according to the positive and negative of the corresponding source-load difference value, the wind and light load sample points in each hour are divided into a surplus power state region set where the source is greater than the load and a power shortage state region set where the source is less than the load; Based on the distance clustering algorithm, the wind and light load sample points of the corresponding surplus power state region set and the power shortage state region set are clustered hour by hour respectively, and after iterative convergence, a full-year typical scenario time set and a corresponding first probability set are determined; According to the full-year typical scenario time set and the first probability set, the first probability corresponding to each typical scenario time is determined respectively; Each of the first probabilities is multiplied by the corresponding number of seasonal days to obtain the frequency of the corresponding typical scenario time in the corresponding hour. According to the frequency, each of the typical scene time repeating permutation combination is formed into summer days ×24 hours, transition season days ×24 hours and winter days ×24 hours corresponding to the disordered boundary sequence, all the disordered boundary sequences are combined into annual disordered boundary sequence; Taking the time sequence of the representative meteorological year data set as the reference, and dividing the representative meteorological year data set according to the same number of seasonal days, the full-year unordered boundary sequence corresponding to each season is sorted and optimized according to the genetic algorithm to generate a full-year scenario boundary with time sequence characteristics. 2.The source-load matching and timing feature based uncertainty scenario generation method of claim 1, wherein, The representative meteorological year data set is determined by the reference year generation method or the typical year construction method based on historical data in previous years. 3.The source-load matching and timing feature based uncertainty scenario generation method of claim 2, wherein, The reference year generation method comprises the following steps: According to the historical data of the target area in at least 5 consecutive years, the average wind speed, annual total radiation, and total load of each year are determined; Based on the parameters of the average wind speed, the annual total radiation, and the total load of the target area in at least 5 consecutive years, the parameters corresponding to each year are sorted respectively to determine the ranking of the corresponding parameters of each year; Based on the ranking of the corresponding parameters of each year, the comprehensive median deviation of each year is determined. Based on the comprehensive median deviation degree of each year ， The historical data of the year with the minimum deviation degree is selected as the representative meteorological year data set; when there are multiple years with the same and minimum deviation degree, the year with a more moderate annual ranking is selected as the representative meteorological year data set according to a preset parameter priority order, and the parameter priority order is the average wind speed, the total annual radiation and the total load. 4.The source-load matching and timing feature uncertainty scenario generation method according to claim 2, wherein, The typical year construction method comprises the following steps: The historical data of the target area in at least 10 consecutive years is preprocessed to determine the daily average value of the first target parameter of each year and each month, and the first target parameter includes solar radiation, wind speed, dry bulb temperature, and humidity; According to the daily average value of each of the first target parameters, the long-term average cumulative distribution function of each year and each month and the single-year cumulative distribution function of each month are determined respectively; According to the mean square error of the single-year cumulative distribution function and the long-term average cumulative distribution function corresponding to each year and each month, and based on months 1 to 12, the month with the smallest mean square error corresponding to the month is selected as the target month; The hour data of the target month corresponding to months 1 to 12 are spliced and smoothed in order to serve as the representative meteorological year data set. 5.The source-load matching and timing feature based uncertainty scenario generation method of claim 1, wherein, The determination of the reference capacity optimization variable set comprises the following steps: Taking the data of the representative meteorological year data set as the system operation boundary, and based on a multi-objective optimization algorithm, the system reference capacity configuration is optimized and solved to determine the reference capacity optimization variable set. 6.The source-load matching and timing feature based uncertainty scenario generation method of claim 1, wherein, The determination of the initial scene time set of the annual wind-solar-load includes the following steps: Based on the historical data of the target region for at least 5 consecutive years, the Gaussian kernel density estimation method is used to fit the second target parameter in each hour to determine the probability density function corresponding to the second target parameter in each hour, and the cumulative distribution function corresponding to the second target parameter in each hour is obtained by integration, the second target parameter including wind power, solar radiation and power load; Based on the cumulative distribution function corresponding to each hour, the Frank-Copula function is used to construct the wind-solar-load three-dimensional joint cumulative distribution function corresponding to the second target parameter in each hour in a hierarchical manner; Based on the wind-solar-load three-dimensional joint cumulative distribution function of each hour, the Monte Carlo or Latin hypercube sampling method is used to generate joint sampling points of each hour, and the corresponding sample values of each joint sampling point in each hour are determined by inverse transformation of the corresponding joint sampling points of each hour, the sample values including the first sample value corresponding to the wind power, the second sample value corresponding to the solar radiation and the third sample value corresponding to the power load, and the first sample value, the second sample value and the third sample value corresponding to each hour are taken as the wind-solar-load sample points in each hour to constitute the initial scene time of wind-solar-load in each hour; The initial scene time of wind-solar-load in each hour is combined by day to constitute the initial all-day scene of wind-solar-load, and is divided into summer, transition season and winter to generate the initial scene time set of wind-solar-load corresponding to 24 hours in summer, 24 hours in transition season and 24 hours in winter respectively, and all the initial scene time sets of wind-solar-load are combined to serve as the initial scene time set of annual wind-solar-load. 7.The source-load matching and timing feature based uncertainty scenario generation method of claim 6, wherein, The determination of the reference capacity optimization variable set includes the following steps: Based on the distance clustering algorithm, all the initial all-day scenes of wind-solar-load are clustered day by day, and after iterative convergence, the annual typical scene day set and the corresponding second probability set are determined; According to the index performance of each typical scene day in the annual typical scene day set, the index performance is determined by weighted summation according to the corresponding second probability set; The index performance is taken as the system operation boundary input, and the reference capacity configuration of the system is optimized and solved based on the multi-objective optimization algorithm to determine the reference capacity optimization variable set. 8.The source-load matching and timing feature uncertainty scenario generation method based on source-load matching and timing feature of claim 1, wherein, The annual wind-solar-load initial scene time set and the annual typical scene time set are divided into summer, transition season and winter, wherein the summer is the 152th to 243th day of the year, the transition season is the 60th to 151th day and the 244th to 334th day of the year, and the winter is the 1st to 59th day and the 335th to 365th day of the year.

9. A method for planning based on source load matching and timing characteristics, the method comprising: determining a plurality of source load matching and timing characteristics; and generating a plan based on the plurality of source load matching and timing characteristics. The annual scene boundary in the uncertainty scenario generation method based on source-load matching and timing characteristics in any one of claims 1 to 8 is taken as the system operation boundary input, and the capacity random planning variable set of the system is determined by optimizing and solving the system capacity configuration based on the multi-objective optimization algorithm. ​

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