CN109599899B - Setting method for new energy operation simulation boundary conditions - Google Patents
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Abstract
The application discloses a setting method of a new energy operation simulation boundary condition, wherein the setting method of the wind power operation simulation boundary condition comprises the following steps: dividing all wind power plants in a region to be detected into a plurality of wind areas according to the geographical position, setting wind speed statistical distribution parameters of the wind power plants according to historical wind speed data, setting wind speed correlation coefficients among the wind areas according to the historical wind speed data and the distance among the wind areas, and setting fan output characteristic parameters according to fan models. Photovoltaic operation boundary condition setting comprises the following steps: setting photovoltaic power station parameters, setting clear sky index distribution parameters, and setting correlation coefficients between photovoltaic power station areas according to distances between the photovoltaic power station areas. By the method and the device, the reliability of boundary condition setting can be improved, and the accuracy of the operation simulation result is further improved.
Description
Technical Field
The application relates to the technical field of new energy grid-connected power generation, in particular to a setting method of new energy operation simulation boundary conditions.
Background
With the development of new energy technology and grid-connected power generation technology, how to apply new energy to grid-connected power generation efficiently and reasonably is a core problem in the aspect of new energy grid-connected power generation. Because the new energy output has certain randomness, volatility and uncertainty, large-scale new energy power generation grid connection has obvious influence on the aspects of the power output structure, the operation form, the operation energy conservation, the economy and the like of the power system, and the planning of the whole power system is influenced.
In order to predict the influence of large-scale new energy grid-connected power generation on an electric power system, a method for establishing a mathematical model is generally adopted in the industry, the influence of new energy on the electric power system is simulated, and the effect of the new energy grid-connected power generation is estimated according to the simulation result. When the model is established, new energy operation simulation boundary conditions need to be set, and the operation simulation boundary conditions determine the operation simulation result, so that setting the simulation boundary conditions for the data model is an important problem.
At present, the method for setting the new energy simulation boundary conditions in the mathematical model generally determines the required parameters according to the current model, and then determines the simulation boundary conditions according to typical parameters, which are determined according to empirical values.
However, in the current method for setting the new energy simulation boundary conditions, because the selected typical parameters are determined only according to empirical values and do not consider the interrelation between actual new energy carriers corresponding to the models, the parameters set by the current method for setting the simulation boundary conditions are not accurate enough, so that the result of operation simulation is influenced, and the evaluation result of large-scale new energy power generation grid connection is not accurate enough.
Disclosure of Invention
The application provides a method for setting a new energy operation simulation boundary condition, which aims to solve the problem that the set parameters in the prior art are not accurate enough.
In order to solve the technical problem, the embodiment of the application discloses the following technical scheme:
a method for setting new energy operation simulation boundary conditions comprises the following steps: setting wind power operation simulation boundary conditions and photovoltaic operation simulation boundary conditions;
the method for setting the wind power operation simulation boundary conditions comprises the following steps:
dividing all wind power plants in a region to be detected into a plurality of wind areas according to the geographical position, wherein each wind area comprises a plurality of wind power plants;
setting wind power plant wind speed statistical distribution parameters according to historical wind speed data, wherein the historical wind speed data is an average value of the historical wind speed data of the wind power plant in the current wind area;
setting a wind speed correlation coefficient among a plurality of wind areas according to historical wind speed data and the distance among the wind areas;
setting fan output characteristic parameters according to the fan model;
the photovoltaic operation simulation boundary condition setting method comprises the following steps:
setting parameters of a photovoltaic power station, wherein the parameters of the photovoltaic power station are used for calculating theoretical solar irradiation intensity and photovoltaic power station output under the condition of no shielding;
setting clear sky index distribution parameters, wherein the clear sky index distribution parameters are used for reflecting clear sky indexes related to photovoltaic power generation;
and setting correlation coefficients between the photovoltaic power station areas according to the distance between the photovoltaic power station areas.
Optionally, the setting of the wind speed statistical distribution parameter of the wind farm according to the historical wind speed data includes:
calculating fitted wind speed statistical distribution parameters of each wind power plant in any wind area of the plurality of wind areas;
and according to the capacity of each wind power plant, taking a weighted average value of the fitted wind speed statistical distribution parameters, and calculating to obtain the wind speed statistical distribution parameters of any wind area.
Optionally, the calculating a fitted wind speed statistical distribution parameter of each wind farm in any one of the plurality of wind zones includes:
according to historical wind speed data of the wind power plant, fitting scale parameters, shape parameters and autocorrelation attenuation coefficients of wind speed distribution of the wind power plant by adopting a Weibull distribution function;
and calculating a monthly characteristic per unit curve and a daily characteristic per unit curve of the wind speed of the wind power plant according to the historical wind speed data of the wind power plant.
Optionally, the setting a wind speed statistical distribution parameter of the wind farm according to the historical wind speed data further includes:
and when no historical wind speed data exists in any wind area, taking the wind speed statistical distribution parameter of an adjacent wind area as the wind speed statistical distribution parameter of any wind area, wherein the adjacent subarea is the wind area which is closest to any wind area in the geographical position.
Optionally, the setting a wind speed correlation coefficient between a plurality of wind zones according to the historical wind speed data and the distance between the wind zones includes:
judging whether historical wind speed data exist in the current wind area or not;
if so, fitting a wind speed correlation coefficient between any two wind areas according to the historical wind speed data of the current wind area;
if not, according to the distance between any two wind areas, utilizing
And calculating to obtain a wind speed correlation coefficient between any two wind areas, wherein c is the wind speed correlation coefficient, d is the distance between any two wind areas, and M is a wind speed correlation coefficient attenuation factor along with the distance.
Optionally, the photovoltaic power plant parameters include: the method comprises the steps that the geographical position, the type of the photovoltaic array, the inclination angle of the photovoltaic array, the direction angle of the photovoltaic array, the installed capacity, the efficiency and the percentage of the predicted absolute error to the capacity of the device are used for calculating the theoretical solar irradiation intensity under the condition of no shielding, and the percentage of the installed capacity, the efficiency and the predicted absolute error to the capacity of the device are used for calculating the output of the photovoltaic power station.
Optionally, the clear sky index distribution parameter includes: clear sky index probability distribution parameter, clear sky index probability distribution parameter lamda value, clear sky index probability distribution parameter theta value, atmospheric scattering coefficient p, atmospheric scattering coefficient q value and bottom surface reflectivity.
Optionally, the setting of the correlation coefficient between the photovoltaic power station regions according to the distance between the photovoltaic power station regions includes:
judging whether the distance between any two photovoltaic power station areas is more than or equal to 200 km;
if yes, setting the correlation coefficient between any two photovoltaic power station areas to be 0.15;
and if not, setting the correlation coefficient between any two photovoltaic power station areas to be 0.4.
The technical scheme provided by the embodiment of the application can have the following beneficial effects:
the application provides a setting method of a new energy operation simulation boundary condition, which mainly comprises setting of a wind power operation simulation boundary condition and setting of a photovoltaic operation boundary condition. According to the setting method of the boundary condition of the wind power operation simulation, a wind power plant is divided into a plurality of wind areas according to geographic positions, and then wind speed statistical distribution parameters, wind speed correlation coefficients and fan output characteristic parameters of the wind power plant are set. The photovoltaic operation boundary condition setting method mainly comprises the steps of setting photovoltaic power station parameters, clear sky index distribution parameters and correlation coefficients among photovoltaic power station areas. When the wind power operation simulation boundary conditions are set, the geographical position relations between the current wind area and other wind areas are fully considered, the correlation coefficient is determined according to different geographical position relations, the obtained setting result is more accurate, the wind speed statistical distribution parameters of each wind area are not simple average values, the capacity of the wind power plant is also considered, the wind speed statistical distribution parameters of the whole wind area are obtained by adopting a function fitting mode for all the wind power plants in the wind area, and the accuracy of the boundary condition setting result is improved. When the boundary condition of photovoltaic power generation operation simulation is set, parameters of the photovoltaic power station such as solar irradiation intensity and output of the photovoltaic power station and clear sky index distribution parameters are fully considered, various parameters influencing photovoltaic power generation are covered more comprehensively, geographical position relations among photovoltaic power station areas are fully considered, the boundary condition of photovoltaic operation simulation can be set from the angle of mutual influence between the photovoltaic power station and adjacent photovoltaic power stations, reliability of boundary condition setting is improved, and accuracy of the influence on operation simulation results is improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flowchart of a method for setting a new energy operation simulation boundary condition according to an embodiment of the present disclosure.
Fig. 2 is a schematic diagram of distribution of correlation coefficients between photovoltaic output shading factors of each observation point in the embodiment of the present application.
Detailed Description
In order to make those skilled in the art better understand the technical solutions in the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
For a better understanding of the present application, embodiments of the present application are explained in detail below with reference to the accompanying drawings.
Referring to fig. 1, fig. 1 is a schematic flowchart of a method for setting a new energy operation simulation boundary condition according to an embodiment of the present disclosure. As can be seen from fig. 1, the method for setting the new energy operation simulation boundary condition in this embodiment mainly includes: the method for setting the wind power operation simulation boundary conditions and the method for setting the photovoltaic operation simulation boundary conditions.
The method for setting the wind power operation simulation boundary conditions mainly comprises the following steps:
s11: according to the geographical position, all wind power plants in the area to be tested are divided into a plurality of wind areas, and each wind area comprises a plurality of wind power plants.
S12: and setting wind speed statistical distribution parameters of the wind power plant according to the historical wind speed data. The historical wind speed data is an average value of the historical wind speed data of the wind power plant in the current wind area.
The historical wind speed data of the wind power plant can be obtained by back-deducing according to the historical wind power output data.
Specifically, step S12 includes the following processes:
s121: and calculating the fitted wind speed statistical distribution parameters of each wind power plant in any wind zone of the plurality of wind zones.
Step S121 mainly includes the following steps:
s1211: and fitting the scale parameter, the shape parameter and the autocorrelation attenuation coefficient of the wind speed distribution of the wind power plant by adopting a Weibull distribution function according to the historical wind speed data of the wind power plant.
S1212: and calculating a monthly characteristic per unit curve and a daily characteristic per unit curve of the wind speed of the wind power plant according to the historical wind speed data of the wind power plant.
The Weibull distribution function is a unimodal, two-parameter distribution function cluster, and in this embodiment, the Weibull distribution function is used, and the probability density function is
In this embodiment, the probability distribution of the wind speed within one year can be calculated by determining the scale parameter c and the shape parameter k. In this embodiment, a two-parameter Weibull distribution function is preferably selected, and the wind speed boundary condition of the wind farm can be rapidly and more accurately obtained by using the two-parameter Weibull distribution function, so that the efficiency and the accuracy of setting the wind power operation simulation boundary condition are improved.
S122: and according to the capacity of each wind power plant, taking a weighted average value of the fitted wind speed statistical distribution parameters, and calculating to obtain the wind speed statistical distribution parameters of any wind area.
In the embodiment, the wind speed statistical distribution parameters in the current wind area are set by adopting a weighted average method, so that the influence of the capacity and weight of each wind power plant in the current wind area on the distribution parameters of the whole wind area can be fully considered, more accurate boundary parameters can be obtained, and the accuracy and reliability of the simulation operation result can be improved. After the wind speed statistical distribution parameters of the current wind area are determined, all future wind power plants in the same wind area range adopt the simulated wind speed data, namely: the wind speeds of the wind power plants in the same wind area are set to be completely related.
Further, in this embodiment, the method for setting statistical distribution parameters of wind speed of a wind farm according to historical wind speed data further includes step S123: and when no historical wind speed data exists in any wind area, taking the wind speed statistical distribution parameters of the adjacent wind areas as the wind speed statistical distribution parameters of any wind area. Wherein the adjacent subarea is a wind area which is closest to any wind area in geographical position.
As can be seen from fig. 1, the method for setting boundary conditions of wind power operation simulation in this embodiment further includes step S13: and setting a wind speed correlation coefficient among a plurality of wind areas according to the historical wind speed data and the distance among the wind areas.
Specifically, step S13 includes the following processes:
s131: and judging whether historical wind speed data exist in the current wind area.
Specifically, whether at least one wind power plant in the current wind area has historical output data or historical wind speed data is judged.
If the current wind zone has historical wind speed data, executing step S132: and fitting a wind speed correlation coefficient between any two wind areas according to the historical wind speed data of the current wind area.
If the current wind zone has historical wind speed data, executing step S133: according to the distance between any two wind zones, use
And calculating to obtain the wind speed correlation coefficient between any two wind areas. Wherein c is a wind speed correlation coefficient, d is a distance between any two wind areas, and M is a wind speed correlation coefficient attenuation factor along with the distance. That is, when all wind farms in the current wind zone have no historical output data or historical wind speed data, step S133 is executed. According to the fitted correlation coefficients between the wind zones, the value of M can be estimated, and then the wind speed correlation coefficient between any two wind zones can be calculated by combining the distances of the wind zone areas.
The wind speed relevance of the wind power piece region is mainly related to the geographical distance, and the wind speed of the wind power piece region with the shorter distance shows stronger relevance due to the influence of the same weather condition; and the wind power districts with longer distances have lower probability of meeting the same weather conditions, so that the wind speed correlation is weaker, and the set boundary conditions can be more accurate by utilizing the negative exponential relationship between the wind speed correlation coefficient and the wind power districts.
S14: and setting fan output characteristic parameters according to the fan model.
The output characteristic parameters of the fan are mainly determined by the model of the fan, and can be determined by specifically referring to the technical documents of the current fan.
Because the processing characteristic parameters of different types of fans are not greatly different, the typical values shown in table 1 can be taken as the fan output characteristic parameters.
TABLE 1 typical wind farm Fan parameters
The method for setting the wind power operation simulation boundary conditions is described below by taking the wind power plant planning of the Shandong power grid as an example:
firstly, the wind power plant is divided into 17 wind areas according to the grade city to which the wind power plant belongs, and the wind areas are respectively as follows: the ciinan wind area, the Qingdao wind area, the catalpo wind area, the jujube bank wind area, the Dongying wind area, the tobacco bench wind area, the Weifang wind area, the Jining wind area, the Taian wind area, the Weihai wind area, the sunshine wind area, the Bizhou wind area, the Texas wind area, the chatting wind area, the Linyi wind area, the Lizefeng area and the Laiwu wind area. And setting the wind speeds of all wind power plants in each wind power zone to be basically the same at the same moment. When the random difference equation is used for simulating the time sequence output of the wind power plant, 17 wind speed areas which are different from each other and take the correlation into consideration are generated simultaneously for each time interval.
And then, distributing the wind speed probability distribution of each wind area according to Weibull, and fitting a scale parameter c, a shape parameter k, an autocorrelation attenuation coefficient and correlation coefficients among the wind speed distributions of different wind areas according to historical output data of the wind power plant of Shandong province. The wind speed parameters of the wind power plant in Shandong province are shown in table 2, the wind speed correlation coefficient of the wind power plant in Shandong province is shown in table 3, and the selection of the fan parameters is shown in table 1.
TABLE 2 wind speed parameters of wind farm in Shandong province
TABLE 3 wind speed correlation coefficient of wind farm in Shandong province
As can be seen from fig. 1, the method for setting the photovoltaic operation simulation boundary condition in the present embodiment mainly includes the following steps:
s21: and setting parameters of the photovoltaic power station, wherein the parameters of the photovoltaic power station are used for calculating the theoretical solar irradiation intensity and the output of the photovoltaic power station under the condition of no shielding.
According to the output model of the photovoltaic panel, the photovoltaic output is in direct proportion to the actual solar irradiation intensity under the condition of no shielding, and when the solar irradiation intensity is the standard irradiation intensity, the photovoltaic panel is rated output.
The photovoltaic power station parameters used for calculating the theoretical solar radiation intensity under the non-shielding condition include: geographic location, photovoltaic array type, photovoltaic array tilt angle, and photovoltaic array azimuth angle. The geographic locations include: the longitude, the latitude and the altitude of the position of the photovoltaic power station, in this embodiment, the same geographic position is adopted for the same photovoltaic power station parcel. Photovoltaic array types include: fixed tilt, horizontal single axis, tilt single axis, and dual axis. The inclination angle of the photovoltaic array is set as the latitude where the photovoltaic power station area is located. The photovoltaic array azimuth angle is set to 0.
The photovoltaic power plant parameters used for calculating photovoltaic power plant processing include: installed capacity, efficiency, and predicted absolute error as a percentage of the capacity of the device. The installed capacity comprises the number of photovoltaic panels and rated power of the photovoltaic panels, the efficiency is the availability ratio of the photovoltaic panels, and the percentage of the predicted absolute error in the capacity of the device is set to be 15%.
S22: and setting clear sky index distribution parameters, wherein the clear sky index distribution parameters are used for reflecting clear sky indexes related to photovoltaic power generation.
The clear sky index refers to a ratio of actual solar radiation lightness to theoretical solar radiation intensity, and is generally related to external uncertain factors such as temperature, cloud cover shielding, weather changes and the like. Specifically, the clear sky index distribution parameters include: a case index probability distribution parameter C, a case index probability distribution parameter lamda value, a clear sky index probability distribution parameter theta value, an atmospheric scattering coefficient p, an atmospheric scattering coefficient q value and a bottom surface reflectivity.
In this embodiment, the set values of the clear sky index distribution parameters are distributed as follows:
the case exponential probability distribution parameter C was set to 0.2994, the case exponential probability distribution parameter lamda was set to 5.062, the clear sky exponential probability distribution parameter theta was set to 0.0343, the atmospheric scattering coefficient p was set to 1.0303, the atmospheric scattering coefficient q was set to 1.1515, and the bottom reflectivity was set to 0.2.
In this embodiment, the weather classification parameter may be obtained by counting the historical weather conditions, and the weather classification parameter shown in the following table 4 is adopted in this embodiment:
TABLE 4 weather Classification parameters
Of course, the parameters set in table 4 may be revised by comparing the simulated photovoltaic use hours with the designed photovoltaic use hours, so as to further improve the accuracy of setting the weather classification parameters.
S23: and setting correlation coefficients between the photovoltaic power station areas according to the distance between the photovoltaic power station areas.
Specifically, step S23 includes the following processes:
s231: and judging whether the distance between any two photovoltaic power station areas is more than or equal to 200 km.
If the distance between any two photovoltaic power station districts is more than or equal to 200km, executing the step S232: the correlation coefficient between any two photovoltaic power plant sections was set to 0.15.
If the distance between any two photovoltaic power station areas is less than 200km, executing the step S233: the correlation coefficient between any two photovoltaic power station sections is set to 0.4.
In this embodiment, reference is made to fig. 2 for a correlation coefficient between photovoltaic output shading factors of each observation point.
The method for setting the photovoltaic operation simulation boundary conditions is described below by taking the photovoltaic power generation plan of the Shandong power grid as an example:
firstly, the photovoltaic power station is divided into 17 photovoltaic power station districts according to the local city to which the photovoltaic power station belongs, namely: the light areas are respectively: the optical area of the optical film is selected from a Jinan optical area, a Qingdao optical area, a Zibo optical area, a Zizhuang optical area, an Dongying optical area, a cigarette bench optical area, a Weifang optical area, a Jining optical area, a Weihai optical area, a sunshine optical area, a Binzhou optical area, a Texas optical area, a chatting optical area, a near-Yici optical area, a Heze optical area and a Laiwu optical area. And setting the weather conditions in each photovoltaic power station area as follows: the photovoltaic shading factors are basically the same at the same time, and the geographic positions are the same. The output of the photovoltaic power station under the theoretical condition can be calculated by utilizing the geographic position, namely: when the photovoltaic power station output in a fine day state is used for simulating photovoltaic shielding factors by using a random difference equation, 17 photovoltaic shielding factors considering correlation are generated at the same time for each time interval, and the generated photovoltaic shielding factors correspond to different light areas.
Then, according to the geographical position of the Shandong photovoltaic power station and the existing historical output data, the typical value of the meteorological parameters is set by the clear sky index distribution parameters, the geographical parameters of each light area and the optimal inclination angle of the photovoltaic power station are set as shown in table 5, the weather type distribution probability of each light area is set as shown in table 6, the technical parameters of the photovoltaic power station are set as shown in table 7, and the related coefficients of the shielding factors of each area are set as shown in table 8.
TABLE 5 geographical parameters of each light zone and optimal inclination setting of photovoltaic power station
TABLE 6 weather type distribution probability of each light zone
TABLE 7 technical parameter setting table for Shandong province photovoltaic power station
TABLE 8 relevant coefficient of shading factor of each area of Shandong province photovoltaic power station
The above description is merely exemplary of the present application and is presented to enable those skilled in the art to understand and practice the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (4)
1. A method for setting new energy operation simulation boundary conditions is characterized by comprising the following steps: setting wind power operation simulation boundary conditions and photovoltaic operation simulation boundary conditions;
the method for setting the wind power operation simulation boundary conditions comprises the following steps:
dividing all wind power plants in a region to be detected into a plurality of wind areas according to the geographical position, wherein each wind area comprises a plurality of wind power plants;
setting wind power plant wind speed statistical distribution parameters according to historical wind speed data, wherein the historical wind speed data is an average value of the historical wind speed data of the wind power plant in the current wind area;
setting a wind speed correlation coefficient among a plurality of wind areas according to historical wind speed data and the distance among the wind areas;
setting fan output characteristic parameters according to the fan model;
the photovoltaic operation simulation boundary condition setting method comprises the following steps:
setting parameters of a photovoltaic power station, wherein the parameters of the photovoltaic power station are used for calculating theoretical solar irradiation intensity and photovoltaic power station output under the condition of no shielding;
setting clear sky index distribution parameters, wherein the clear sky index distribution parameters are used for reflecting clear sky indexes related to photovoltaic power generation;
setting a correlation coefficient between photovoltaic power station areas according to the distance between the photovoltaic power station areas;
wherein, according to historical wind speed data, setting wind power plant wind speed statistical distribution parameters, including:
calculating fitted wind speed statistical distribution parameters of each wind power plant in any wind area of the plurality of wind areas; according to the capacity of each wind power plant, taking a weighted average value of the fitted wind speed statistical distribution parameters, and calculating to obtain the wind speed statistical distribution parameters of any wind area;
the method for setting the wind speed statistical distribution parameters of the wind power plant according to the historical wind speed data further comprises the following steps:
when no historical wind speed data exists in any wind area, taking the wind speed statistical distribution parameter of an adjacent wind area as the wind speed statistical distribution parameter of the wind area, wherein the adjacent wind area is the wind area which is closest to the wind area in the geographical position;
the setting of the wind speed correlation coefficient among the wind zones according to the historical wind speed data and the distance among the wind zones comprises the following steps:
judging whether historical wind speed data exist in the current wind area or not; if so, fitting a wind speed correlation coefficient between any two wind areas according to the historical wind speed data of the current wind area; if not, according to the distance between any two wind areas, utilizing
Calculating to obtain a wind speed correlation coefficient between any two wind areas, wherein c is the wind speed correlation coefficient, d is the distance between any two wind areas, and M is a wind speed correlation coefficient attenuation factor along with the distance;
according to the distance between the photovoltaic power station districts, set up the correlation coefficient between the photovoltaic power station districts, include:
judging whether the distance between any two photovoltaic power station areas is more than or equal to 200 km;
if yes, setting the correlation coefficient between any two photovoltaic power station areas to be 0.15;
and if not, setting the correlation coefficient between any two photovoltaic power station areas to be 0.4.
2. The method for setting the boundary condition of the new energy operation simulation, according to claim 1, wherein the calculating the fitted wind speed statistical distribution parameter of each wind farm in any one of the plurality of wind zones comprises:
according to historical wind speed data of the wind power plant, fitting scale parameters, shape parameters and autocorrelation attenuation coefficients of wind speed distribution of the wind power plant by adopting a Weibull distribution function;
and calculating a monthly characteristic per unit curve and a daily characteristic per unit curve of the wind speed of the wind power plant according to the historical wind speed data of the wind power plant.
3. The method for setting the boundary condition of the new energy operation simulation, according to claim 1, wherein the photovoltaic power station parameters comprise: the method comprises the steps that the geographical position, the type of the photovoltaic array, the inclination angle of the photovoltaic array, the direction angle of the photovoltaic array, the installed capacity, the efficiency and the percentage of the predicted absolute error to the capacity of the device are used for calculating the theoretical solar irradiation intensity under the condition of no shielding, and the percentage of the installed capacity, the efficiency and the predicted absolute error to the capacity of the device are used for calculating the output of the photovoltaic power station.
4. The method according to claim 1, wherein the clear sky index distribution parameters include: clear sky index probability distribution parameter, clear sky index probability distribution parameter lamda value, clear sky index probability distribution parameter theta value, atmospheric scattering coefficient p, atmospheric scattering coefficient q value and bottom surface reflectivity.
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| CN104794325A (en) * | 2015-03-10 | 2015-07-22 | 国家电网公司 | Colony wind power plant output timing sequence simulation method based on random difference equation |
| CN105005937A (en) * | 2015-04-28 | 2015-10-28 | 国家电网公司 | Photovoltaic power station output sequential simulation method based on clearness indexes |
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