Method and system for constructing new energy output and load scenarios based on meteorological factors

Abstract

The application provides a system for constructing a new energy output and load scene based on meteorological factors, and the method comprises the following steps: constructing a time-series meteorological probability distribution model through historical meteorological data, and generating a simulated meteorological scene through random sampling; and determining a new energy output scene model and a load scene model through historical output data of the new energy, historical load data of the power load, first historical meteorological data, and the type of meteorological type, and determining an output correction coefficient interval and a power load correction coefficient interval under each meteorological type, so as to determine a new energy output scene and a power load scene constructed under the simulated meteorological scene according to an output correction coefficient matrix and a power load correction coefficient matrix, the new energy output scene model and the load scene model. The method and the system fully consider the climate correlation of the source and load in the new power system mainly composed of new energy when modeling the source and load scenes, and have important significance.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of data analysis under new power systems, and more particularly, to a method and system for constructing new energy output and load scenarios based on meteorological factors. BACKGROUND

[0002] It is of great significance to construct a scenario model of a new power system mainly based on new energy. With the increasing penetration of wind and solar power, the uncertainty of wind and solar power output and the trend of peak load of electricity consumption are becoming more and more prominent, which has a huge impact on the safe and stable operation of the power system. At the same time, the significant climate correlation between wind and solar power output and electricity consumption leads to the intensification of the climate sensitivity and vulnerability of the power system. Therefore, designing a scenario generation method considering the uncertainty and correlation of source and load is of great significance to the flexibility and reliability of the power system.

[0003] At present, scholars at home and abroad have carried out extensive research on scenario generation methods considering the uncertainty and correlation of source and load. A large number of random samples are used to describe the uncertainty of source and load, and clustering reduction and other methods are used to extract typical scenarios with correlation. However, none of them considers the climate correlation of source and load in scenario generation modeling from the perspective of climate uncertainty, and effectively links the uncertainty, correlation and time sequence of source and load, making it difficult to simulate new energy output and electricity consumption with time sequence meteorological characteristics. SUMMARY

[0004] In order to solve the technical problems that the existing scenario generation methods considering the uncertainty and correlation of source and load do not consider the climate correlation of source and load and lack of description of time sequence meteorological characteristics, the present application provides a method and system for constructing new energy output and load scenarios based on meteorological factors.

[0005] According to one aspect of the present application, the present application provides a method for constructing new energy output and load scenarios based on meteorological factors, which comprises:

[0006] Collecting historical output data of new energy for which a new energy output scenario is to be constructed, historical load data of electricity load for which a load scenario is to be constructed, first historical meteorological data of the region where the new energy and the electricity load are located, and second historical meteorological data for dividing meteorological types;

[0007] Based on the second historical meteorological data, determining the types of divided meteorological types according to the set meteorological factors affecting the new energy output and the electricity load;

[0008] Determining the types of meteorological types contained in the region where the new energy and the electricity load are located and the weight of each meteorological type according to the first historical meteorological data and the types of divided meteorological types;

[0009] determine a time-series weather probability distribution model of the region according to the types of weather included in the region of the new energy and the power load and the weight of each type of weather;

[0010] generate a new energy output scenario according to the first historical weather data, the weight of the types of weather included in the region, and the historical output data of the new energy, and generate a load scenario according to the first historical weather data, the types of weather included in the region, and the historical load data of the power load;

[0011] determine an output correction coefficient interval of the new energy under each type of weather according to the historical output data of the new energy and the types of weather included in the region, and determine an electricity load correction coefficient interval of the power load under each type of weather according to the historical load data of the power load and the types of weather included in the region of the power load;

[0012] randomly sample the time-series weather probability distribution model of the region to generate a simulated weather scenario of the region;

[0013] determine a new energy output scenario constructed under the simulated weather scenario of the region according to the new energy output scenario model and the output correction coefficient interval of the new energy under each type of weather, and determine a load scenario constructed under the simulated weather scenario of the region according to the load scenario model and the electricity load correction coefficient interval of the power load under each type of weather.

[0014] According to another aspect of the present application, the present application provides a system for constructing new energy output and load scenarios based on weather factors, the system comprising:

[0015] a data collection module for collecting historical output data of a new energy for which a new energy output scenario is to be constructed, historical load data of a power load for which a load scenario is to be constructed, first historical weather data of a region in which the new energy and the power load are located, and second historical weather data for dividing weather types;

[0016] a first weather module for determining the types of divided weather types according to set weather factors affecting the new energy output and the power load based on the second historical weather data;

[0017] a second weather module for determining the types of weather included in the region in which the new energy and the power load are located and the weight of each type of weather according to the first historical weather data and the types of divided weather;

[0018] a first model module configured to determine a time-series weather probability distribution model of a region where the new energy source and the power load are located according to a type of weather included in the region and a weight of each type of weather;

[0019] a second model module configured to generate a new energy output scenario model according to the first historical weather data, the weight of the type of weather included in the region, and historical output data of the new energy source, and generate a load scenario model according to the first historical weather data, the type of weather included in the region, and historical load data of the power load;

[0020] a correction coefficient module configured to determine an output correction coefficient interval of the new energy source under each type of weather according to the historical output data of the new energy source and the type of weather included in the region, and determine an electricity load correction coefficient interval of the power load under each type of weather according to the historical load data of the power load and the type of weather included in the region where the power load is located;

[0021] a first scenario module configured to randomly sample the time-series weather probability distribution model of the region to generate a simulated weather scenario of the region;

[0022] a second scenario module configured to determine a new energy output scenario constructed under the simulated weather scenario of the region according to the new energy output scenario model and the output correction coefficient interval of the new energy source under each type of weather, and determine a load scenario constructed under the simulated weather scenario of the region according to the load scenario model and the electricity load correction coefficient interval of the power load under each type of weather.

[0023] According to another aspect of the present application, a computer readable medium having stored thereon a computer program is provided, the program implementing the steps of any of the methods for constructing a new energy output and load scenario based on weather factors according to the present application when executed by a processor.

[0024] According to another aspect of the present application, an electronic device is provided, comprising:

[0025] one or more processors;

[0026] a storage device configured to store one or more programs,

[0027] when the one or more programs are executed by the one or more processors, the one or more processors implement the steps of any of the methods for constructing a new energy output and load scenario based on weather factors according to the present application.

[0028] The present invention describes a system for constructing new energy output and load scenarios based on meteorological factors. The method includes: identifying and classifying meteorological types using extensive second historical meteorological data; constructing a time-series meteorological probability distribution model based on historical meteorological data of the regions where the proposed new energy output and load scenarios are located, and generating simulated meteorological scenarios through random sampling; and determining the new energy output scenario model and load scenario model, as well as the output correction coefficient range and power load correction coefficient range under each meteorological type, based on the historical output data of new energy, the historical load data of power load, the first historical meteorological data, and the meteorological type. This allows for the determination of the new energy output scenario and power load scenario constructed under the simulated meteorological scenario based on the output correction coefficient matrix, the power load correction coefficient matrix, the new energy output scenario model, and the load scenario model. The method and system described above fully consider the climate correlation of source load in a new power system dominated by new energy sources when modeling source load scenarios. They establish an effective connection between source load uncertainty, correlation, and temporal sequence, thereby realizing the simulation of new energy output scenarios and power load scenarios with temporal meteorological characteristics. This provides sufficient samples for the study of power system flexibility and reliability, which is of great significance. Attached Figure Description

[0029] Exemplary embodiments of the present invention can be more fully understood by referring to the following figures:

[0030] Figure 1 A flowchart illustrating a method for constructing new energy output and load scenarios based on meteorological factors according to a preferred embodiment of the present invention;

[0031] Figure 2 This is a schematic diagram of the structure of a system for constructing new energy output and load scenarios based on meteorological factors according to a preferred embodiment of the present invention. Detailed Implementation

[0032] Exemplary embodiments of the invention will now be described with reference to the accompanying drawings. However, the invention may be embodied in many different forms and is not limited to the embodiments described herein. These embodiments are provided to fully and completely disclose the invention and to fully convey its scope to those skilled in the art. The terminology used in the exemplary embodiments illustrated in the drawings is not intended to limit the invention. In the drawings, the same units / elements are referred to by the same reference numerals.

[0033] The terms used herein, including technical terms, have meanings commonly understood by those skilled in the art, unless otherwise specified. In addition, it is understood that the terms defined in commonly used dictionaries should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so indicated.

[0034] Example method

[0035] Figure 1 A flow chart of a method for constructing new energy output and load scenarios based on meteorological factors according to a preferred embodiment of the present application. As shown in Figure 1 The method for constructing new energy output and load scenarios based on meteorological factors according to the preferred embodiment of the present application starts from step 101.

[0036] In step 101, historical output data of new energy for which a new energy output scenario is to be constructed, historical load data of power load for which a load scenario is to be constructed, first historical meteorological data of the region where the new energy and power load are located, and second historical meteorological data for dividing meteorological types are collected.

[0037] In the preferred embodiment, the second historical meteorological data for dividing meteorological types is larger in range than the first historical meteorological data. For example, the former is meteorological data of different seasons nationwide, while the latter can be meteorological data of a certain season in a certain region. By collecting meteorological data of a larger range for correlation study, meteorological types can be more accurately identified and divided. Thus, when constructing a time-series meteorological probability distribution model of a certain region, the types and weights of local meteorological types can be more accurately and efficiently determined based on historical meteorological data of the region.

[0038] In step 102, based on the second historical meteorological data, the types of the divided meteorological types are determined according to the set meteorological factors that affect new energy output and power load.

[0039] Preferably, the determination of the types of the divided meteorological types based on the second historical meteorological data and according to the set meteorological factors that affect new energy output and power load includes:

[0040] M meteorological factors that affect new energy output and power load are set;

[0041] The initial sample data of the M meteorological factors in the second historical meteorological data are normalized to obtain normalized second sample data;

[0042] The second sample data is sorted according to numerical value to obtain third sample data;

[0043] The third sample data is divided into a plurality of sub-sample data according to the sorting;

[0044] According to each sub-sample data, ranks R i and R j of i-th and j-th meteorological factors in M meteorological factors are determined, wherein 1≤i, j≤M;

[0045] According to the ranks R i and R j , standard deviations σ(R i ) and σ(R j ) of the i-th and j-th meteorological factors and a covariance cov(R i , R j ) between the i-th and j-th meteorological factors are calculated respectively;

[0046] According to the standard deviations σ(R i ), σ(R j ) and the covariance cov(R i , R j ), a rank correlation coefficient ρ ij of the i-th and j-th meteorological factors is calculated;

[0047] When the rank correlation coefficients ρ ij values determined according to the sub-sample data are all greater than a set correlation coefficient threshold, it is determined that there is a strong correlation between the meteorological factors of the sub-sample data, and the sub-sample data is a meteorological type;

[0048] According to the number of sub-samples in which there is a strong correlation between the meteorological factors, a kind of the divided meteorological type is determined.

[0049] In the preferred embodiment, the new energy output mainly refers to wind power output and photovoltaic output. The wind power output is directly affected by wind speed, the photovoltaic output is mainly affected by light intensity, and the load is mainly affected by temperature. The cloud and rain conditions and the temperature directly affect the light intensity. Therefore, the wind speed, the cloud and rain conditions and the temperature are taken as the meteorological factors affecting the new energy output and the power load, and the meteorological types are divided based on the meteorological data. There is usually a certain correlation between the wind speed, the cloud and rain conditions and the temperature, and the wind speed and the light intensity are both non-normal distribution random variables. Therefore, the Spearman rank correlation coefficient matrix is used to represent the correlation therebetween, and finally 13 possible meteorological types shown in Table 1 are obtained.

[0050] Table 1

[0051]

[0052] In step 103, the types of weather included in the region where the new energy and the power load are located are determined according to the first historical weather data and the types of weather division, and the weight of each type of weather.

[0053] In the preferred embodiment, since the weather division is based on nationwide weather data, the types of weather included may not be the same in different seasons in different regions, and the weight of each type of weather included may not be the same. For example, for the 13 types of climate division, the region where the new energy output scenario and the load scenario are constructed may only include 12 types of weather in a certain period of time, and the proportion of the 12 types of weather is not the same. For the weather type with the largest proportion, it is assumed to be the regular weather type in the region in the period.

[0054] In step 104, the time sequence weather probability distribution model of the region where the new energy and the power load are located is determined according to the types of weather included in the region and the weight of each type of weather.

[0055] Preferably, the time sequence weather probability distribution model of the region where the new energy and the power load are located is determined according to the types of weather included in the region and the weight of each type of weather, wherein the expression of the time sequence weather probability distribution model is:

[0056]

[0057]

[0058]

[0059] In the formula, k0 is the number of types of weather included in the region where the new energy and the power load are located, π k is the weight of the kth type of weather in the region where the new energy and the power load are located, p(x|μ k , δ k ) is the kth weather type in the Gaussian mixture model, μ k represents the mean of k, and δ k represents the variance of k.

[0060] In step 105, the new energy output scenario model is generated according to the first historical weather data, the weight of the types of weather included in the region, and the historical output data of the new energy, and the load scenario model is generated according to the first historical weather data, the types of weather included in the region, and the historical load data of the power load.

[0061] Preferably, the new energy output scenario model is generated according to the first historical meteorological data, the weight of the type of meteorological condition contained in the region, and the historical output data of the new energy, and the load scenario model is generated according to the first historical meteorological data, the type of meteorological condition contained in the region, and the historical load data of the power load, including:

[0062] The third historical meteorological data corresponding to the meteorological condition with the largest weight in the type of meteorological condition contained in the region is selected from the first historical meteorological data;

[0063] The new energy output scenario model is determined according to the third historical meteorological data and the historical output data of the new energy corresponding to the third historical meteorological data;

[0064] The load scenario model is generated according to the third historical meteorological data and the historical load data of the power load corresponding to the third historical meteorological data.

[0065] In the preferred embodiment, the meteorological condition with the largest weight in the type of meteorological condition contained in the region is determined as a regular meteorological condition, and then the corresponding historical meteorological data and the historical load data of the new energy under the regular meteorological condition are used as sample data for modeling, so as to determine the new energy output scenario model and the load scenario model under the regular meteorological condition of the region, wherein:

[0066] The expression of the wind power output scenario model is:

[0067]

[0068] In the formula, P WT is the fan power, P WTr is the rated power of the fan, v ci , v r , and v co are the cut-in wind speed, the rated wind speed, and the cut-out wind speed of the fan, respectively;

[0069] The expression of the photovoltaic output scenario model is:

[0070]

[0071] In the formula, alp is the photovoltaic power temperature coefficient, I real is the corrected solar radiation intensity, m represents all the normal working photovoltaic installed capacity, T a is the corrected photovoltaic working temperature, and T tis the standard photovoltaic working temperature, I0 is the rated light intensity, the solar irradiance is generated by various parameters of the photovoltaic module, including the solar irradiance steady-state component, the solar irradiance fluctuation component, the latitude, the tilt angle and the azimuth angle, the above parameters change with the geographical location of the photovoltaic power station in part except the measurable ones, and the optimal layout parameters of photovoltaic power stations in various places can be referred to. After the solar irradiance I is calculated, since it is served by the beta distribution, the light intensity is corrected by using the beta distribution to obtain the corrected solar irradiance I real , and the calculation formula is:

[0072] I real = betarnd [a, b, 1, length (I)] x I

[0073] In the formula, a and b are two opening parameters of the beta distribution of the photovoltaic output, which are calculated by statistical analysis of the historical photovoltaic output data of the region.

[0074]

[0075]

[0076] In the formula, σ is the standard deviation of the historical photovoltaic output, and μ is the expectation.

[0077] The determination of the load scenario model of the power load and the generation method of the wind power output scenario model are similar, and will not be described here.

[0078] In step 106, the output correction coefficient interval of the new energy under each meteorological type is determined according to the historical output data of the new energy and the types of meteorological types contained in the region, and the electricity load correction coefficient interval of the power load under each meteorological type is determined according to the historical load data of the power load and the types of meteorological types contained in the region where the power load is located.

[0079] Preferably, the output correction coefficient interval of the new energy under each meteorological type is determined according to the historical output data of the new energy and the types of meteorological types contained in the region, and the electricity load correction coefficient interval of the power load under each meteorological type is determined according to the historical load data of the power load and the types of meteorological types contained in the region where the power load is located.

[0080] According to the types of meteorological types contained in the region, the average maximum output p i,max and the average minimum output p i,min of the historical output data of the new energy under the i-th meteorological type are determined.

[0081] According to the p i,maxand p i,min and the maximum output p of the new energy unit s determining the output correction coefficient interval [s x,min , s x,max ] of the new energy under the i-th meteorological type, wherein the expressions of s x,min and s x,max are respectively as follows:

[0082] s x,min = p i,min / p s

[0083] s x,max = p i,max / p s

[0084] determining the average maximum power load L i,max and the average minimum power load L i,min of the power load under the i-th meteorological type according to the types of meteorological types contained in the region;

[0085] determining the power load correction coefficient interval [L x,min , L x,max ] of the power load under the i-th meteorological type according to the L i,max and L i,min , and the rated maximum load L s of the power load, wherein the expressions of L x,min and L x,max are respectively as follows:

[0086] L x,min = L i,min / L s

[0087] L x,max = L i,max / L s .

[0088] In step 107, the time-series meteorological probability distribution model of the region is randomly sampled to generate a simulated meteorological scenario of the region.

[0089] In the preferred embodiment, the Monte Carlo random sampling method is used to randomly sample the constructed time-series meteorological probability distribution model in time periods to obtain the meteorological scenario corresponding to the selected time period.

[0090] At step 108, the new energy output scene under the simulated weather scene of the region is determined according to the new energy output scene model and the output correction coefficient interval of the new energy under each weather type, and the load scene under the simulated weather scene of the region is determined according to the load scene model and the electricity load correction coefficient interval of the power load under each weather type.

[0091] Preferably, the new energy output scene under the simulated weather scene of the region is determined according to the new energy output scene model and the output correction coefficient interval of the new energy under each weather type, and the load scene under the simulated weather scene of the region is determined according to the load scene model and the electricity load correction coefficient interval of the power load under each weather type, including:

[0092] The weather type corresponding to each time period in the simulated weather scene of the region is determined according to time sequence;

[0093] Based on the weather type corresponding to each time period, an output correction coefficient is randomly selected from the output correction coefficient interval of the new energy under each weather type to generate the output correction coefficient sequence of the new energy in the simulated weather scene of the region, and an electricity load correction coefficient is randomly selected from the electricity load correction coefficient interval of the power load under each weather type to generate the electricity load correction coefficient sequence of the power load in the simulated weather scene of the region;

[0094] The output correction coefficients in the output correction coefficient sequence are multiplied by the new energy output scene model respectively to generate the new energy output scene under the simulated weather scene of the region, and the electricity load correction coefficients in the electricity load correction coefficient sequence are multiplied by the load scene model respectively to generate the load scene under the simulated weather scene of the region.

[0095] In the preferred embodiment, by collecting second historical meteorological data of meteorological factors affecting new energy output and power load under a large number of different weather conditions, a Spearman rank correlation coefficient matrix is used to represent the correlation between the influencing factors of source and load under different weather types, so as to identify and finely divide the typical weather types in different seasons. According to the first historical meteorological data of the source and load in the region where the new energy output scenario and the load scenario are to be constructed, a time series weather probability distribution model is constructed based on the divided weather types, and a simulated weather scenario is generated by Monte Carlo random sampling to describe the weather time series uncertainty. According to the historical weather data with the largest proportion weight under the first historical weather data, and the corresponding new energy historical output and historical load data, the new energy output scenario model and the load scenario model under the conventional weather condition are determined, and according to the first historical weather data and the corresponding new energy historical output and historical load data, the output correction coefficient interval and the power load correction coefficient interval under each weather type are determined, and finally the new energy output scenario and the load scenario under the simulated weather scenario in the region are generated. The method fully considers the climate correlation of source and load in the new power system dominated by new energy in the source and load scenario modeling, effectively connects the source and load uncertainty, correlation and time series, so as to realize the simulation of new energy output scenario and power load scenario with time series weather characteristics, provide sufficient samples for the research of power system flexibility and reliability, and have important significance.

[0096] Example system

[0097] Figure 2 The structure schematic diagram of the system for constructing new energy output and load scenario based on meteorological factors according to the preferred embodiment of the present application is shown in FIG. 1. Figure 2 As shown in FIG. 1, the system 200 for constructing new energy output and load scenario based on meteorological factors according to the preferred embodiment of the present application comprises:

[0098] A data acquisition module 201 is configured to acquire historical output data of new energy for which a new energy output scenario is to be constructed, historical load data of power load for which a load scenario is to be constructed, first historical weather data of the region where the new energy and the power load are located, and second historical weather data for dividing weather types.

[0099] A first weather module 202 is configured to determine the types of divided weather types based on the second historical weather data and according to set weather factors affecting new energy output and power load.

[0100] A second weather module 203 is configured to determine the types of weather types contained in the region where the new energy and the power load are located and the weight of each weather type according to the first historical weather data and the types of divided weather types.

[0101] The first model module 204 is configured to determine a time-series weather probability distribution model of the region according to a type of weather included in the region where the new energy and the power load are located and a weight of each type of weather;

[0102] The second model module 205 is configured to generate a new energy output scenario model according to the first historical weather data, the weight of the type of weather included in the region, and historical output data of the new energy, and generate a load scenario model according to the first historical weather data, the type of weather included in the region, and historical load data of the power load;

[0103] The correction coefficient module 206 is configured to determine an output correction coefficient interval of the new energy under each type of weather according to the historical output data of the new energy and the type of weather included in the region where the new energy is located, and determine an electricity load correction coefficient interval of the power load under each type of weather according to the historical load data of the power load and the type of weather included in the region where the power load is located;

[0104] The first scenario module 207 is configured to randomly sample the time-series weather probability distribution model of the region to generate a simulated weather scenario of the region;

[0105] The second scenario module 208 is configured to determine a new energy output scenario constructed under the simulated weather scenario of the region according to the new energy output scenario model and the output correction coefficient interval of the new energy under each type of weather, and determine a load scenario constructed under the simulated weather scenario of the region according to the load scenario model and the electricity load correction coefficient interval of the power load under each type of weather.

[0106] Preferably, the first weather module 202 is configured to determine the type of divided weather according to a set weather factor affecting the new energy output and the power load based on the second historical weather data, including:

[0107] M weather factors affecting the new energy output and the power load are set;

[0108] The initial sample data of the M weather factors in the second historical weather data is normalized to obtain normalized second sample data;

[0109] The second sample data is sorted according to the numerical value to obtain third sample data;

[0110] The third sample data is divided into a plurality of sub-sample data according to the sorting;

[0111] The rank R of the i th and j th weather factors in the M weather factors is determined according to each sub-sample datai and R j , where 1≤i, j≤M;

[0112] According to the rank R i and R j , the standard deviations σ(R i ) and σ(R j ) of the i-th and j-th meteorological factors, and the covariance cov(R i , R j ) between the i-th and j-th meteorological factors are calculated respectively;

[0113] According to the standard deviations σ(R i ), σ(R j ) and the covariance cov(R i , R j ), the rank correlation coefficient ρ ij of the i-th and j-th meteorological factors is calculated;

[0114] When all the rank correlation coefficients ρ ij values determined according to the sub-sample data are greater than the set correlation coefficient threshold value, it is determined that there is a strong correlation between the meteorological factors of the sub-sample data, which is a meteorological type;

[0115] According to the number of sub-samples with strong correlation between meteorological factors, the type of the divided meteorological type is determined.

[0116] Preferably, the first model module 204 determines the time-series meteorological probability distribution model of the region according to the type of meteorological types contained in the region where the new energy and power load is located, and the weight of each meteorological type, wherein the expression of the time-series meteorological probability distribution model is:

[0117]

[0118]

[0119]

[0120] In the formula, k0 is the number of types of meteorological types contained in the region where the new energy and power load is located, π k is the weight of the k-th meteorological type in the region where the new energy and power load is located, p(x|μ k , δ k ) is the k-th weather type in the Gaussian mixture model, μ k represents the mean of k, and δ k represents the variance of k.

[0121] Preferably, the second model module 205 generates a new energy output scenario model according to the first historical meteorological data, the weights of the types of meteorological types contained in the region, and the historical output data of the new energy, and generates a load scenario model according to the first historical meteorological data, the types of meteorological types contained in the region, and the historical load data of the power load, including:

[0122] selecting third historical meteorological data corresponding to a meteorological type with the largest weight from the types of meteorological types contained in the region from the first historical meteorological data;

[0123] determining a new energy output scenario model according to the third historical meteorological data and historical output data of the new energy corresponding to the third historical meteorological data;

[0124] generating a load scenario model according to the third historical meteorological data and historical load data of the power load corresponding to the third historical meteorological data.

[0125] Preferably, the correction coefficient module 206 determines an output correction coefficient interval of the new energy under each meteorological type according to the historical output data of the new energy and the types of meteorological types contained in the region where the new energy is located, and determines an electricity consumption load correction coefficient interval of the power load under each meteorological type according to the historical load data of the power load and the types of meteorological types contained in the region where the power load is located, including:

[0126] determining the average maximum output p i,max and the average minimum output p i,min of the historical output data of the new energy under the i-th meteorological type according to the types of meteorological types contained in the region;

[0127] determining the output correction coefficient interval [s x,min , s x,max ] of the new energy under the i-th meteorological type according to the p i,max and p i,min , and the rated maximum output p s of the unit of the new energy, wherein the expressions of s x,min and s x,max are respectively:

[0128] s x,min = p i,min / p s

[0129] s x,max = p i,max / p s

[0130] determining the average maximum power consumption load L of the historical load data of the power load under the i-th meteorological type according to the kind of meteorological types contained in the region i,max and the average minimum power consumption load L i,min ;

[0131] determining the power consumption load correction coefficient interval [L i,max , L i,min ] of the power load under the i-th meteorological type according to the L s , L x,min , and the rated maximum load L x,max of the power load, wherein the expressions of the L x,min and the L x,max are respectively:

[0132] L x,min = L i,min / L s

[0133] L x,max = L i,max / L s .

[0134] Preferably, the second scenario module 208 determines the new energy output scenario constructed under the simulated meteorological scenario of the region according to the new energy output scenario model and the output correction coefficient interval of the new energy under each meteorological type, and determines the load scenario constructed under the simulated meteorological scenario of the region according to the load scenario model and the power consumption load correction coefficient interval of the power load under each meteorological type, comprising:

[0135] determining the meteorological type corresponding to each time period in the simulated meteorological scenario of the region in time sequence;

[0136] based on the meteorological type corresponding to each time period, randomly selecting an output correction coefficient from the output correction coefficient interval of the new energy under each meteorological type to generate the output correction coefficient sequence of the new energy in the simulated meteorological scenario of the region, and randomly selecting a power consumption load correction coefficient from the power consumption load correction coefficient interval of the power load under each meteorological type to generate the power consumption load correction coefficient sequence of the power load in the simulated meteorological scenario of the region;

[0137] multiplying the output correction coefficients in the output correction coefficient sequence by the new energy output scenario model respectively to generate the new energy output scenario under the simulated meteorological scenario of the region, and multiplying the power consumption load correction coefficients in the power consumption load correction coefficient sequence by the load scenario model respectively to generate the load scenario under the simulated meteorological scenario of the region.

[0138] The system for constructing new energy output and load scenarios based on meteorological factors according to the preferred embodiment constructs new energy output scenarios considering the correlation between source and climate based on historical meteorological data, new energy historical output data, and historical load data. The steps for constructing the load scenarios are the same as those in the method for constructing new energy output and load scenarios based on meteorological factors, and the technical effects achieved are also the same, which will not be described here.

[0139] Preferably, the present application further provides a computer readable medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the steps in any of the methods for constructing new energy output and load scenarios based on meteorological factors according to the present application.

[0140] Preferably, the present application further provides an electronic device comprising:

[0141] one or more processors;

[0142] a storage device for storing one or more programs,

[0143] when the one or more programs are executed by the one or more processors, the one or more processors implement the steps in any of the methods for constructing new energy output and load scenarios based on meteorological factors according to the present application.

[0144] The present application has been described with reference to a few embodiments. However, those skilled in the art will appreciate that other embodiments than those described herein are equally possible within the scope of the present application, as defined by the appended claims.

[0145] Generally, all terms used in the claims are to be interpreted according to their ordinary meaning in the technical field, unless explicitly defined otherwise herein. All references to "a" or "an" means "at least one" unless otherwise clearly indicated by the context of use. The steps of any methods disclosed herein need not be performed in the exact order disclosed, unless explicitly stated.

[0146] Those skilled in the art will appreciate that embodiments of the present application can be devised for use with systems other than the systems described herein. Embodiments of the present application can be embodied in a variety of ways, including as a method, as a system, or as a computer program product. Accordingly, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, magnetic disks, CD-ROMs, optical storage media such as DVD s, etc.) embodying computer program code thereon for use by a computer or processor.

[0147] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure One one or more flow or blocks Figure One one or more flow or blocks

[0148] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart block or blocks. Figure One one or more flow or blocks Figure One one or more flow or blocks

[0149] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure One one or more flow or blocks Figure One Figure One one or more flow or blocks

[0150] Finally, it should be noted that the above-mentioned embodiments are merely intended to illustrate the technical solutions of the present application, but not to limit the same. Although the present application has been described in detail with reference to the above-mentioned embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or equivalent replaced without departing from the spirit and scope of the present application, and any modification or equivalent replacement should be covered within the scope of protection of the claims of the present application.

Claims

1. A method for constructing new energy output and load scenarios based on meteorological factors, characterized in that, The method includes: Historical power output data of the new energy source to be constructed for the proposed new energy power output scenario, historical load data of the power load to be constructed for the proposed load scenario, first historical meteorological data of the region where the new energy source and power load are located, and second historical meteorological data for classifying meteorological types are collected. Based on the second historical meteorological data, the types of meteorological categories are determined according to the meteorological factors that affect the output of new energy sources and the power load. Based on the first historical meteorological data and the classification of meteorological types, determine the types of meteorological types included in the regions where the new energy sources and power loads are located, as well as the weight of each meteorological type; The temporal meteorological probability distribution model of the region is determined based on the types of meteorological types in the region where the new energy and power load are located, and the weight of each meteorological type. Based on the first historical meteorological data, the weight of the types of meteorological types in the region, and the historical output data of the new energy source, a new energy output scenario model is generated; based on the first historical meteorological data, the types of meteorological types in the region, and the historical load data of the power load, a load scenario model is generated. The output correction coefficient range of the new energy source under each weather type is determined based on the historical output data of the new energy source and the types of weather conditions in the region where it is located; and the power load correction coefficient range of the power load under each weather type is determined based on the historical load data of the power load and the types of weather conditions in the region where the power load is located. Random sampling is performed on the temporal meteorological probability distribution model of the region to generate a simulated meteorological scenario for the region; Based on the new energy output scenario model and the output correction coefficient range of the new energy under each weather type, a new energy output scenario is constructed under the simulated weather scenario of the region. Based on the load scenario model and the power load correction coefficient range of the power load under each weather type, a load scenario is constructed under the simulated weather scenario of the region.

2. The method according to claim 1, characterized in that, Based on the second historical meteorological data, and according to the meteorological factors affecting new energy output and power load, the types of meteorological categories are determined, including: Set M meteorological factors that affect the output of new energy sources and the power load; The initial sample data of M meteorological factors in the second historical meteorological data are normalized to obtain the normalized second sample data. The second sample data is sorted according to its numerical value to obtain the third sample data; The third sample data is divided into several sub-sample data according to the sorting. Determine the rank R of the i-th and j-th meteorological factors among the M meteorological factors based on the data of each subsample. i and R j Where 1≤i, j≤M; According to rank R i and R j Calculate the standard deviation σ(R) of the i-th and j-th meteorological factors respectively. i ) and σ(R j ), and the covariance cov(R) between the i-th and j-th meteorological factors. i ,R j ); According to the standard deviation σ(R) i ), σ(R) j ) and covariance cov(R i ,R j Calculate the rank correlation coefficient ρ between the i-th and j-th meteorological factors. ij ; When the rank correlation coefficient ρ is determined based on the subsample data ij When all values ​​are greater than the set correlation coefficient threshold, it is determined that there is a strong correlation between the meteorological factors in the subsample data, which is a meteorological type. The types of meteorological categories are determined based on the number of subsamples with strong correlations among meteorological factors.

3. The method according to claim 1, characterized in that, The process involves determining a time-series meteorological probability distribution model for the region where the new energy source and power load are located, based on the types of meteorological types present and the weight of each meteorological type. The expression for this time-series meteorological probability distribution model is as follows: In the formula, k0 represents the number of meteorological types present in the region where the new energy source and power load are located, and π k p(x|μ) represents the weight of the k-th meteorological type in the region where the new energy source and power load are located. k ,δ k Let ) represent the k-th weather type in the Gaussian mixture model, and μ k δ represents the mean of k. k Let k represent the variance.

4. The method according to claim 1, characterized in that, The step of generating a new energy output scenario model based on the weights of the types of meteorological data in the region and the historical output data of the new energy source, and generating a load scenario model based on the weights of the types of meteorological data in the region and the historical load data of the power load, includes: Select the third historical meteorological data corresponding to the meteorological type with the highest weight among the meteorological types included in the region from the first historical meteorological data; The new energy output scenario model is determined based on the third historical meteorological data and the corresponding historical output data of new energy sources. A load scenario model is generated based on the third historical meteorological data and the historical load data of the power load corresponding to the third historical meteorological data.

5. The method according to claim 1, characterized in that, The process of determining the output correction factor range of the new energy source under each weather type based on its historical output data and the types of weather conditions in the region; and determining the electricity load correction factor range of the electricity load under each weather type based on its historical load data and the types of weather conditions in the region where the electricity load is located, includes: Based on the types of weather conditions in the region, determine the average maximum output p of the historical power output data of the new energy source under the i-th weather type. i,max and average minimum output p i,min ; According to the p i,max and p i,min And the rated maximum output p of the new energy unit s Determine the output correction coefficient range [s] for the i-th meteorological type of the new energy source. x,min s x,max ], wherein the s x,min and s x,max The expressions are as follows: s x,min =p i,min / p s s x,max =p i,max / p s Based on the types of weather conditions in the region, determine the average maximum electricity load L of the historical load data for the i-th weather type. i,max and average minimum electrical load L i,min ; According to the L i,max and L i,min and the rated maximum load L of the electrical load. s Determine the range of the power load correction factor [L] for the power load under the i-th weather type. x,min L x,max ], wherein the L x,min and L x,max The expressions are as follows: L x,min =L i,min / L s L x,max =L i,max / L s 。 6. The method according to claim 1, characterized in that, The process of determining the new energy output scenario constructed under the simulated meteorological scenario of the region based on the new energy output scenario model and the output correction coefficient range of the new energy under each meteorological type, and determining the load scenario constructed under the simulated meteorological scenario of the region based on the load scenario model and the electricity load correction coefficient range of the electricity load under each meteorological type, includes: Determine the weather type corresponding to each time period in the simulated weather scenario for the region according to the time sequence; Based on the meteorological type corresponding to each time period, an output correction coefficient is randomly selected from the output correction coefficient range of the new energy source under each meteorological type to generate the output correction coefficient sequence of the new energy source in the simulated meteorological scenario of the region. Similarly, an electricity load correction coefficient is randomly selected from the electricity load correction coefficient range of the power load under each meteorological type to generate the electricity load correction coefficient sequence of the power load in the simulated meteorological scenario of the region. The output correction coefficients in the output correction coefficient sequence are multiplied by the new energy output scenario model to generate a new energy output scenario under the simulated meteorological scenario of the region. The electricity load correction coefficients in the electricity load correction coefficient sequence are multiplied by the load scenario model to generate a load scenario under the simulated meteorological scenario of the region.

7. A system for constructing new energy output and load scenarios based on meteorological factors, characterized in that, The system includes: The data acquisition module is used to collect historical power output data of the new energy source to be constructed in the new energy power output scenario, historical load data of the power load to be constructed in the load scenario, first historical meteorological data of the region where the new energy source and power load are located, and second historical meteorological data for classifying meteorological types. The first meteorological module is used to determine the types of meteorological categories based on the second historical meteorological data and according to the meteorological factors that affect the output of new energy sources and the power load. The second meteorological module is used to determine the types of meteorological types included in the region where the new energy and power load are located, as well as the weight of each meteorological type, based on the first historical meteorological data and the types of meteorological types classified. The first model module is used to determine the time-series meteorological probability distribution model of the region based on the types of meteorological types contained in the region where the new energy and power load are located, and the weight of each meteorological type. The second model module is used to generate a new energy output scenario model based on the first historical meteorological data, the weight of the types of meteorological types in the region, and the historical output data of the new energy source, and to generate a load scenario model based on the first historical meteorological data, the types of meteorological types in the region, and the historical load data of the power load. The correction coefficient module is used to determine the output correction coefficient range of the new energy source under each weather type based on the historical output data of the new energy source and the types of weather types in the region where it is located; and to determine the power load correction coefficient range of the power load under each weather type based on the historical load data of the power load and the types of weather types in the region where the power load is located. The first scenario module is used to randomly sample the temporal meteorological probability distribution model of the region and generate a simulated meteorological scenario for the region. The second scenario module is used to determine the new energy output scenario constructed under the simulated meteorological scenario of the region based on the new energy output scenario model and the output correction coefficient range of the new energy under each meteorological type, and to determine the load scenario constructed under the simulated meteorological scenario of the region based on the load scenario model and the power load correction coefficient range of the power load under each meteorological type.

8. The system according to claim 7, characterized in that, The first meteorological module, based on the second historical meteorological data and according to the meteorological factors affecting new energy output and power load, determines the types of meteorological categories, including: Set M meteorological factors that affect the output of new energy sources and the power load; The initial sample data of M meteorological factors in the second historical meteorological data are normalized to obtain the normalized second sample data. The second sample data is sorted according to its numerical value to obtain the third sample data; The third sample data is divided into several sub-sample data according to the sorting. Determine the rank R of the i-th and j-th meteorological factors among the M meteorological factors based on the data of each subsample. i and R j Where 1≤i, j≤M; According to rank R i and R j Calculate the standard deviation σ(R) of the i-th and j-th meteorological factors respectively. i ) and σ(R j ), and the covariance cov(R) between the i-th and j-th meteorological factors. i ,R j ); According to the standard deviation σ(R) i ), σ(R) j ) and covariance cov(R i ,R j Calculate the rank correlation coefficient ρ between the i-th and j-th meteorological factors. ij ; When the rank correlation coefficient ρ is determined based on the subsample data ij When all values ​​are greater than the set correlation coefficient threshold, it is determined that there is a strong correlation between the meteorological factors in the subsample data, which is a meteorological type. The types of meteorological categories are determined based on the number of subsamples with strong correlations among meteorological factors.

9. A computer-readable medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1-6.

10. An electronic device, characterized in that, include: One or more processors; Storage device for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1-6.

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