New energy power generation output characteristic evaluation method and device, medium and terminal

Abstract

The invention discloses a new energy power generation output characteristic evaluation method and device, a medium and a terminal, relates to the technical field of power systems, and mainly aims to solve the problem of low applicability of an existing new energy power generation output characteristic evaluation result. Comprising the following steps: acquiring wind power, photovoltaic and wind-solar complementary hourly output data of a target power station in multiple years, and taking the years as target years one by one; according to the wind power, photovoltaic and wind-solar complementary hourly output data of the target year, calculating a wind power annual full-generation utilization hour number, a photovoltaic annual full-generation utilization hour number and a wind-solar complementary annual full-generation utilization hour number respectively; sorting wind power, photovoltaic and wind-solar complementary annual full generation utilization hours in each year, and selecting a year corresponding to a median order in a sequence as a typical year; and according to the hourly output data of the typical years, respectively generating output characteristic evaluation results of multiple time scales.

Description

Technical Field

[0001] The present application relates to the technical field of power systems, and in particular to a method and device, medium, and terminal for evaluating output characteristics of renewable energy power generation. Background Art

[0002] As the country vigorously promotes the construction of new power systems, the proportion of renewable energy generation in the power grid continues to increase. In order to ensure the safe and stable operation of the power grid, it is necessary to accurately evaluate the output characteristics of renewable energy generation.

[0003] In the prior art, the original load fluctuation sequence, wind and solar output fluctuation sequence and net load fluctuation sequence of each daily analysis condition are obtained to analyze and determine the evaluation results of new energy power generation output characteristics such as daily electricity index and daily regulation index.

[0004] However, since only the wind and solar power output characteristics on a daily scale are analyzed, the time dimension is relatively single, resulting in low applicability of the generated evaluation results of the output characteristics of renewable energy power generation. Summary of the invention

[0005] In view of this, the present application provides a method and device, medium, and terminal for evaluating the output characteristics of renewable energy power generation, the main purpose of which is to solve the problem of low applicability of existing evaluation results of the output characteristics of renewable energy power generation.

[0006] According to one aspect of the present application, a method for evaluating the output characteristics of renewable energy power generation is provided, comprising:

[0007] Obtain the hourly wind power output data, photovoltaic power output data, and wind-solar hybrid power output data of the target power station in multiple years, and use each year as the target year;

[0008] According to the hourly output data of wind power, photovoltaic power and wind-solar complementarity in the target year, calculate the annual full utilization hours of wind power, the annual full utilization hours of photovoltaic power and the annual full utilization hours of wind-solar complementarity respectively;

[0009] The annual full utilization hours of wind power, photovoltaic power, and wind-solar complementarity in each year are sorted separately, and the year corresponding to the median rank in the sequence is selected as the typical year;

[0010] According to the hourly output data of wind power, the hourly output data of photovoltaic power, and the hourly output data of wind-solar complementary power in the typical years, the annual-scale output characteristic assessment results, monthly-scale output characteristic assessment results, daily-scale output characteristic assessment results, and hourly-scale output characteristic assessment results for wind power generation are generated respectively; the annual-scale output characteristic assessment results, monthly-scale output characteristic assessment results, daily-scale output characteristic assessment results, and hourly-scale output characteristic assessment results for photovoltaic power generation; the annual-scale output characteristic assessment results, monthly-scale output characteristic assessment results, daily-scale output characteristic assessment results, and hourly-scale output characteristic assessment results for wind-solar complementary power generation are generated respectively.

[0011] Preferably, the annual scale output characteristic evaluation results include a cumulative power generation-output coefficient-guarantee rate curve chart, and a curve chart of output coefficient changes under different guarantee rates; the monthly scale output characteristic evaluation results include a monthly average output coefficient bar chart and a monthly maximum output coefficient line chart, a monthly maximum and minimum continuous weekly hours power generation deviation rate line chart, and a weekly power quantity probability distribution curve chart; the daily scale output characteristic evaluation results include a monthly maximum and minimum daily power generation deviation rate line chart, and a daily power quantity probability distribution curve chart; the hourly scale output characteristic evaluation results include an hourly output variation line chart.

[0012] Preferably, the generating of the corresponding cumulative power generation-output coefficient-guarantee rate curve graph and the output coefficient change curve graph under different guarantee rates according to the hourly wind power output data or the hourly photovoltaic output data or the hourly wind-solar complementary output data of the typical year includes:

[0013] Arrange the hourly output data of the typical year in descending order to obtain an hourly output data sequence, wherein the hourly output data is any one of wind power hourly output data, photovoltaic hourly output data, and wind-solar complementary hourly output data;

[0014] Based on the stratified interval power calculation formula, the stratified interval power corresponding to the sequence is calculated according to the hourly output data of each sequence in the hourly output data sequence;

[0015] Taking each sequence as the target sequence one by one, accumulating the stratified interval power of the target sequence and the stratified interval power of all sequences before the target sequence to obtain the cumulative power of the target sequence and the cumulative power of each sequence;

[0016] Based on the frequency guarantee rate calculation formula, the frequency guarantee rate corresponding to each rank is calculated respectively;

[0017] Generate a cumulative power generation-output coefficient-guarantee rate curve chart based on the cumulative power generation, hourly output data, and frequency guarantee rate of each rank;

[0018] And, dividing the hourly output data according to the number of hours in a day to obtain multiple groups of daily hourly output data groups;

[0019] Extracting the hourly output data at the same time from each of the daily hourly output data groups to form multiple groups of hourly output data at the same time, and taking each group of hourly output data at the same time as the target hourly output data at the same time;

[0020] Sorting the hourly output data contained in the target moment hourly output data group to obtain a target moment hourly output data sequence, and determining the maximum output moment, average output moment, and minimum output moment of the target moment hourly output data group from the target moment hourly output data sequence;

[0021] According to the peak output moment, average output moment and minimum output moment of each hourly output data group at the same time, a curve diagram of output coefficient variation under different guarantee rates is generated.

[0022] Preferably, the generating of the corresponding monthly average output coefficient bar graph and monthly maximum output coefficient line graph, monthly maximum and minimum continuous weekly hours power generation deviation rate line graph, and weekly power generation probability distribution curve graph based on the hourly wind power output data or photovoltaic hourly output data or wind-solar complementary hourly output data of the typical year includes:

[0023] Dividing the hourly output data of the typical year according to the number of hours in a month to obtain multiple groups of monthly hourly output data;

[0024] Calculate the monthly average output coefficient corresponding to each group of monthly hourly output data respectively;

[0025] Respectively select the monthly maximum output data in each group of the monthly hourly output data, and calculate the monthly maximum output coefficient corresponding to the monthly maximum output data;

[0026] Generate a monthly average output coefficient bar graph and a monthly maximum output coefficient line graph according to each of the monthly average output coefficients and the monthly maximum output coefficients;

[0027] and, taking each of the monthly hourly output data groups as the target monthly hourly output data group;

[0028] Slidingly combining the target month hourly output data group according to the weekly hours to obtain multiple groups of weekly hourly output data groups, calculating the weekly hourly power generation data group corresponding to each group of the weekly hourly output data group, and summing the hourly power generation data in each group of the weekly hourly power generation data group to obtain multiple weekly hourly total power generation data, and calculating the first mean of the multiple weekly hourly total power generation data;

[0029] Filter out the maximum weekly hourly total power generation data and the minimum weekly hourly total power generation data from the plurality of weekly hourly total power generation data, and calculate the first maximum deviation rate and the first minimum deviation rate according to the first mean value;

[0030] Generate a line graph of monthly maximum and minimum continuous weekly hours power generation deviation rate according to the first maximum deviation rate and the first minimum deviation rate corresponding to each target monthly hourly output data group;

[0031] and dividing the hourly output data of the typical year according to the number of weekly hours to obtain multiple groups of weekly hourly output data, calculating the weekly hourly power generation data group corresponding to each group of the weekly hourly output data group, and summing the hourly power generation data in each group of the weekly hourly power generation data group to obtain multiple weekly total power generation data;

[0032] The plurality of weekly total power generation data are grouped into first intervals according to a first preset grouping distance, and a first probability density of each first group is calculated, so as to generate a weekly power generation probability distribution curve diagram according to the first probability density of each first group.

[0033] Preferably, the generating of the corresponding monthly maximum and minimum daily power generation deviation rate line graph and daily power generation probability distribution curve graph based on the hourly wind power output data or photovoltaic hourly output data or wind-solar complementary hourly output data of the typical year includes:

[0034] Dividing the hourly output data of the typical year according to the number of hours in a day to obtain multiple groups of daily hourly output data groups;

[0035] Calculating the daily hourly power generation data group corresponding to each group of the daily hourly output data group, and summing the hourly power generation data in each group of the daily hourly power generation data group to obtain a plurality of daily total power generation data, and calculating a second mean value of the plurality of daily total power generation data;

[0036] Divide the multiple daily total power generation data by month to obtain multiple groups of monthly daily total power generation data, and use each group of monthly daily total power generation data as a target monthly daily total power generation data group;

[0037] Filtering out the maximum daily total power generation data and the minimum daily total power generation data from the target month daily power generation data group, and calculating the second maximum deviation rate and the second minimum deviation rate according to the second mean value;

[0038] Generate a line graph of the monthly maximum and minimum daily power generation deviation rates according to the second maximum deviation rate and the second minimum deviation rate corresponding to each target monthly daily power generation data group;

[0039] Furthermore, the daily total power generation data are grouped into second intervals according to a second preset grouping distance, and the second probability density of each second group is calculated, so as to generate a daily power probability distribution curve diagram according to the second probability density of each second group.

[0040] Preferably, generating a corresponding hourly output amplitude line graph according to the hourly wind power output data or the hourly photovoltaic output data or the hourly wind-solar complementary output data of the typical year includes:

[0041] Based on the hourly output data of the typical year, calculate the hourly output amplitude data;

[0042] The hourly output amplitude variation data is divided into intervals according to a preset interval ratio to obtain an hourly output amplitude variation line graph.

[0043] Preferably, before obtaining the hourly wind power output data, the hourly photovoltaic output data, and the hourly wind-solar complementary output data of the target power station in multiple years, the method further includes:

[0044] Obtain the hourly wind speed data and hourly solar radiation data of the target power station area in multiple years, and calculate the hourly wind power output data, photovoltaic hourly output data, and wind-solar complementary hourly output data based on the wind speed and wind power output conversion formula and the solar radiation and photovoltaic power output conversion formula.

[0045] According to another aspect of the present application, a device for evaluating the output characteristics of renewable energy power generation is provided, comprising:

[0046] A data acquisition module is used to acquire the hourly output data of wind power, photovoltaic power, and wind-solar hybrid power of the target power station in multiple years, and use each year as the target year;

[0047] The annual full generation utilization hours calculation module is used to calculate the annual full generation utilization hours of wind power, the annual full generation utilization hours of photovoltaic power, and the annual full generation utilization hours of wind-solar complementarity according to the hourly output data of wind power, the hourly output data of photovoltaic power, and the hourly output data of wind-solar complementarity in the target year;

[0048] The typical year determination module is used to sort the annual full utilization hours of wind power, the annual full utilization hours of photovoltaic power, and the annual full utilization hours of wind-solar complementarity in each year, and select the year corresponding to the median rank in the sequence as the typical year;

[0049] The output characteristic assessment result generation module is used to generate the annual-scale output characteristic assessment result, monthly-scale output characteristic assessment result, daily-scale output characteristic assessment result and hourly-scale output characteristic assessment result for wind power generation, the annual-scale output characteristic assessment result, monthly-scale output characteristic assessment result, daily-scale output characteristic assessment result and hourly-scale output characteristic assessment result for photovoltaic power generation, and the annual-scale output characteristic assessment result, monthly-scale output characteristic assessment result, daily-scale output characteristic assessment result and hourly-scale output characteristic assessment result for wind-solar complementary power generation according to the hourly wind power output data, hourly photovoltaic output data and hourly wind-solar complementary output data of the typical year.

[0050] Preferably, the annual scale output characteristic evaluation results include a cumulative power generation-output coefficient-guarantee rate curve chart, and a curve chart of output coefficient changes under different guarantee rates; the monthly scale output characteristic evaluation results include a monthly average output coefficient bar chart and a monthly maximum output coefficient line chart, a monthly maximum and minimum continuous weekly hours power generation deviation rate line chart, and a weekly power quantity probability distribution curve chart; the daily scale output characteristic evaluation results include a monthly maximum and minimum daily power generation deviation rate line chart, and a daily power quantity probability distribution curve chart; the hourly scale output characteristic evaluation results include an hourly output variation line chart.

[0051] Preferably, the output characteristic evaluation result generating module is used to:

[0052] Arrange the hourly output data of the typical year in descending order to obtain an hourly output data sequence, wherein the hourly output data is any one of wind power hourly output data, photovoltaic hourly output data, and wind-solar complementary hourly output data;

[0053] Based on the stratified interval power calculation formula, the stratified interval power corresponding to the sequence is calculated according to the hourly output data of each sequence in the hourly output data sequence;

[0054] Taking each sequence as the target sequence one by one, accumulating the stratified interval power of the target sequence and the stratified interval power of all sequences before the target sequence to obtain the cumulative power of the target sequence and the cumulative power of each sequence;

[0055] Based on the frequency guarantee rate calculation formula, the frequency guarantee rate corresponding to each rank is calculated respectively;

[0056] Generate a cumulative power generation-output coefficient-guarantee rate curve chart based on the cumulative power generation, hourly output data, and frequency guarantee rate of each rank;

[0057] And, dividing the hourly output data according to the number of hours in a day to obtain multiple groups of daily hourly output data groups;

[0058] Extracting the hourly output data at the same time from each of the daily hourly output data groups to form multiple groups of hourly output data at the same time, and taking each group of hourly output data at the same time as the target hourly output data at the same time;

[0059] Sorting the hourly output data contained in the target moment hourly output data group to obtain a target moment hourly output data sequence, and determining the maximum output moment, average output moment, and minimum output moment of the target moment hourly output data group from the target moment hourly output data sequence;

[0060] According to the peak output moment, average output moment and minimum output moment of each hourly output data group at the same time, a curve diagram of output coefficient variation under different guarantee rates is generated.

[0061] Preferably, the output characteristic evaluation result generating module is used to:

[0062] Dividing the hourly output data of the typical year according to the number of hours in a month to obtain multiple groups of monthly hourly output data;

[0063] Calculate the monthly average output coefficient corresponding to each group of monthly hourly output data respectively;

[0064] Respectively select the monthly maximum output data in each group of the monthly hourly output data, and calculate the monthly maximum output coefficient corresponding to the monthly maximum output data;

[0065] Generate a monthly average output coefficient bar graph and a monthly maximum output coefficient line graph according to each of the monthly average output coefficients and the monthly maximum output coefficients;

[0066] and, taking each of the monthly hourly output data groups as the target monthly hourly output data group;

[0067] Slidingly combining the target month hourly output data group according to the weekly hours to obtain multiple groups of weekly hourly output data groups, calculating the weekly hourly power generation data group corresponding to each group of the weekly hourly output data group, and summing the hourly power generation data in each group of the weekly hourly power generation data group to obtain multiple weekly hourly total power generation data, and calculating the first mean of the multiple weekly hourly total power generation data;

[0068] Filter out the maximum weekly hourly total power generation data and the minimum weekly hourly total power generation data from the plurality of weekly hourly total power generation data, and calculate the first maximum deviation rate and the first minimum deviation rate according to the first mean value;

[0069] Generate a line graph of monthly maximum and minimum continuous weekly hours power generation deviation rate according to the first maximum deviation rate and the first minimum deviation rate corresponding to each target monthly hourly output data group;

[0070] and dividing the hourly output data of the typical year according to the number of weekly hours to obtain multiple groups of weekly hourly output data, calculating the weekly hourly power generation data group corresponding to each group of the weekly hourly output data group, and summing the hourly power generation data in each group of the weekly hourly power generation data group to obtain multiple weekly total power generation data;

[0071] The plurality of weekly total power generation data are grouped into first intervals according to a first preset grouping distance, and a first probability density of each first group is calculated, so as to generate a weekly power generation probability distribution curve diagram according to the first probability density of each first group.

[0072] Preferably, the output characteristic evaluation result generating module is used to:

[0073] Dividing the hourly output data of the typical year according to the number of hours in a day to obtain multiple groups of daily hourly output data groups;

[0074] Calculating the daily hourly power generation data group corresponding to each group of the daily hourly output data group, and summing the hourly power generation data in each group of the daily hourly power generation data group to obtain a plurality of daily total power generation data, and calculating a second mean value of the plurality of daily total power generation data;

[0075] Divide the multiple daily total power generation data by month to obtain multiple groups of monthly daily total power generation data, and use each group of monthly daily total power generation data as a target monthly daily total power generation data group;

[0076] Filtering out the maximum daily total power generation data and the minimum daily total power generation data from the target month daily power generation data group, and calculating the second maximum deviation rate and the second minimum deviation rate according to the second mean value;

[0077] Generate a line graph of the monthly maximum and minimum daily power generation deviation rates according to the second maximum deviation rate and the second minimum deviation rate corresponding to each target monthly daily power generation data group;

[0078] Furthermore, the daily total power generation data are grouped into second intervals according to a second preset grouping distance, and the second probability density of each second group is calculated, so as to generate a daily power probability distribution curve diagram according to the second probability density of each second group.

[0079] Preferably, the output characteristic evaluation result generating module is used to:

[0080] Based on the hourly output data of the typical year, calculate the hourly output amplitude data;

[0081] The hourly output amplitude variation data is divided into intervals according to a preset interval ratio to obtain an hourly output amplitude variation line graph.

[0082] Preferably, before the data acquisition module, the device further includes:

[0083] The output data conversion module is used to obtain the hourly wind speed data and hourly solar radiation data of the target power station area in multiple years, and calculate the hourly wind power output data, photovoltaic hourly output data, and wind-solar complementary hourly output data based on the wind speed and wind power output conversion formula and the solar radiation and photovoltaic power output conversion formula.

[0084] According to another aspect of the present application, a storage medium is provided, in which at least one executable instruction is stored, and the executable instruction enables a processor to execute operations corresponding to the above-mentioned method for evaluating the output characteristics of renewable energy power generation.

[0085] According to another aspect of the present application, a terminal is provided, comprising: a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other through the communication bus;

[0086] The memory is used to store at least one executable instruction, and the executable instruction enables the processor to execute operations corresponding to the above-mentioned method for evaluating the output characteristics of new energy power generation.

[0087] By means of the above technical solution, the technical solution provided by the embodiment of the present application has at least the following advantages:

[0088] The present application provides a method and device, medium, and terminal for evaluating the output characteristics of renewable energy power generation. First, the hourly wind power output data, the hourly photovoltaic output data, and the hourly wind-solar complementarity output data of the target power station in multiple years are obtained, and each year is used as the target year one by one; secondly, according to the hourly wind power output data, the hourly photovoltaic output data, and the hourly wind-solar complementarity output data of the target year, the annual full generation utilization hours of wind power, the annual full generation utilization hours of photovoltaic power, and the annual full generation utilization hours of wind-solar complementarity are calculated respectively; again, the annual full generation utilization hours of wind power, the annual full generation utilization hours of photovoltaic power, and the annual full generation utilization hours of wind-solar complementarity of each year are sorted respectively, and the median in the sequence is selected. The year corresponding to the sequence is taken as the typical year; finally, based on the hourly wind power output data, photovoltaic hourly output data, and wind-solar complementary hourly output data of the typical year, the annual-scale output characteristic assessment results, monthly-scale output characteristic assessment results, daily-scale output characteristic assessment results, and hourly-scale output characteristic assessment results for wind power generation are generated respectively; the annual-scale output characteristic assessment results, monthly-scale output characteristic assessment results, daily-scale output characteristic assessment results, and hourly-scale output characteristic assessment results for photovoltaic power generation; the annual-scale output characteristic assessment results, monthly-scale output characteristic assessment results, daily-scale output characteristic assessment results, and hourly-scale output characteristic assessment results for wind-solar complementary power generation are generated respectively. Compared with the prior art, the embodiment of the present application selects the year corresponding to the median annual full utilization hours from multiple years as a typical year, and generates annual scale output characteristic evaluation results, monthly scale output characteristic evaluation results, daily scale output characteristic evaluation results, and hourly scale output characteristic evaluation results according to the hourly output data of the typical year. By analyzing the wind and solar output characteristics in multiple time dimensions, the generated new energy output characteristics can reflect the differences and volatility characteristics of new energy output in different periods, thereby improving the applicability of the evaluation results of new energy power generation output characteristics.

[0089] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0090] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present application. Also, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0091] Figure 1 A flow chart of a method for evaluating the output characteristics of renewable energy power generation provided in an embodiment of the present application is shown;

[0092] Figure 2 A flow chart for generating a cumulative power generation-output coefficient-guarantee rate curve diagram provided in an embodiment of the present application is shown;

[0093] Figure 3 A cumulative power generation-output coefficient-guarantee rate curve diagram provided in an embodiment of the present application is shown;

[0094] Figure 4 A flow chart for generating a curve diagram of output coefficient variation under different guarantee rates provided in an embodiment of the present application is shown;

[0095] Figure 5 The output coefficient variation curve diagram under different guarantee rates provided in the embodiment of the present application is shown;

[0096] Figure 6 A flow chart for generating a monthly average output coefficient bar chart and a monthly maximum output coefficient line chart provided in an embodiment of the present application is shown;

[0097] Figure 7 The figure shows a bar graph of the monthly average output coefficient and a line graph of the monthly maximum output coefficient provided in the embodiment of the present application;

[0098] Figure 8 A flow chart for generating a monthly maximum and minimum continuous weekly hours power generation deviation rate line graph provided by an embodiment of the present application is shown;

[0099] Fig. 9 It shows a line graph of the monthly maximum and minimum continuous weekly hours of power generation deviation rate provided by an embodiment of the present application;

[0100] Fig.10 A flow chart for generating a weekly power probability distribution curve diagram provided by an embodiment of the present application is shown;

[0101] Fig.11 A weekly power probability distribution curve diagram provided by an embodiment of the present application is shown;

[0102] Fig.12 A flow chart for generating a line graph of monthly maximum and minimum daily power generation deviation rate provided in an embodiment of the present application is shown;

[0103] Fig.13 A line graph of monthly maximum and minimum daily power generation deviation rates provided in an embodiment of the present application is shown;

[0104] Fig.14 A daily power probability distribution curve diagram provided by an embodiment of the present application is shown;

[0105] Fig.15 The hourly output amplitude variation line chart provided by the embodiment of the present application is shown;

[0106] Fig.16 A block diagram showing the composition of a device for evaluating the output characteristics of renewable energy power generation provided by an embodiment of the present application is shown;

[0107] Fig.17 A schematic diagram of the structure of a terminal provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0108] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0109] At the same time, it should be understood that for the convenience of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship.

[0110] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the present application, its application, or uses.

[0111] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered as part of the specification.

[0112] It should be noted that like reference numerals and letters refer to similar items in the following figures, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0113] The embodiments of the present application can be applied to computer systems / servers, which can operate with many other general or special computing system environments or configurations. Examples of well-known computing systems, environments and / or configurations suitable for use with computer systems / servers include, but are not limited to: personal computer systems, server computer systems, thin clients, thick clients, handheld or laptop devices, microprocessor-based systems, set-top boxes, programmable consumer electronics, network personal computers, small computer systems, large computer systems, and distributed cloud computing technology environments including any of the above systems, etc.

[0114] Computer systems / servers may be described in the general context of computer system executable instructions (such as program modules) executed by a computer system. In general, program modules may include routines, programs, object programs, components, logic, data structures, etc., which perform specific tasks or implement specific abstract data types. Computer systems / servers may be implemented in a distributed cloud computing environment where tasks are performed by remote processing devices linked through a communication network. In a distributed cloud computing environment, program modules may be located on local or remote computing system storage media including storage devices.

[0115] The present application embodiment provides a method for evaluating the output characteristics of renewable energy power generation, such as Figure 1 As shown, the method includes:

[0116] 101. Obtain the hourly wind power output data, photovoltaic power output data, and wind-solar complementary power output data of the target power station in multiple years, and take each year as the target year.

[0117] Among them, the hourly output data of wind power is used to characterize the hourly output data when wind power is used for power generation; the hourly output data of photovoltaic power is used to characterize the hourly output data when photovoltaic power is used for power generation; the hourly output data of wind-solar hybrid power is used to characterize the hourly output data when wind power and photovoltaic power are used for power generation. In the embodiment of the present application, the current execution end can be a data analysis module of the new power system.

[0118] It should be noted that when the hourly output data cannot be obtained directly, the meteorological data of the area where the target power station is located can be obtained first, such as hourly wind speed data, hourly solar radiation data, etc., and then the output is converted according to the meteorological data to obtain the hourly output data.

[0119] 102. Based on the hourly output data of wind power, hourly output data of photovoltaic power, and hourly output data of wind-solar complementarity in the target year, calculate the annual full generation and utilization hours of wind power, the annual full generation and utilization hours of photovoltaic power, and the annual full generation and utilization hours of wind-solar complementarity respectively.

[0120] Among them, the annual full generation utilization hours are used to characterize the quotient between the output data and the equipment power. In the embodiment of the present application, any single year is taken as the object, and the corresponding annual full generation utilization hours of wind power, annual full generation utilization hours of photovoltaic power, and annual full generation utilization hours of wind and solar power are calculated according to its hourly output data of wind power, hourly output data of photovoltaic power, and hourly output data of wind-solar complementation.

[0121] 103. Sort the annual full utilization hours of wind power, the annual full utilization hours of photovoltaic power, and the annual full utilization hours of wind-solar complementarity in each year respectively, and select the year corresponding to the median rank in the sequence as the typical year.

[0122] In the embodiment of the present application, the annual full power utilization hours of multiple years are sorted, and the year corresponding to the median rank in the sequence (i.e., the middle year) is selected as the typical year. It can be understood that when sorting, the annual full power utilization hours of wind power in multiple years, the annual full power utilization hours of photovoltaic power in multiple years, and the annual full power utilization hours of wind-solar hybrid power in multiple years are sorted separately to obtain the typical year of the power generation mode.

[0123] 104. Based on the hourly output data of wind power, the hourly output data of photovoltaic power, and the hourly output data of wind-solar complementary power in typical years, the annual-scale output characteristic assessment results, monthly-scale output characteristic assessment results, daily-scale output characteristic assessment results, and hourly-scale output characteristic assessment results for wind power generation are generated respectively; the annual-scale output characteristic assessment results, monthly-scale output characteristic assessment results, daily-scale output characteristic assessment results, and hourly-scale output characteristic assessment results for photovoltaic power generation; the annual-scale output characteristic assessment results, monthly-scale output characteristic assessment results, daily-scale output characteristic assessment results, and hourly-scale output characteristic assessment results for wind-solar complementary power generation are generated respectively.

[0124] Among them, the annual-scale output characteristics assessment results include the cumulative power generation-output coefficient-guarantee rate curve (used to evaluate the stability of the output), the output coefficient change curve under different guarantee rates (used to evaluate the change of the output coefficient in a day); the monthly-scale output characteristics assessment results include the monthly average output coefficient bar chart and the monthly maximum output coefficient line chart (used to evaluate the range of the monthly output coefficient and the distribution of the months with larger monthly output coefficients), the monthly maximum and minimum continuous weekly hours power generation deviation rate line chart (used to evaluate the fluctuation of power generation for seven consecutive days), and the weekly power probability distribution curve chart (used to evaluate the distribution of weekly full-time hours); the daily-scale output characteristics assessment results include the monthly maximum and minimum daily power generation deviation rate line chart (used to evaluate the fluctuation of daily power generation), and the daily power probability distribution curve chart (used to evaluate the distribution of daily full-time hours); the hourly-scale output characteristics assessment results include the hourly output variation line chart (used to evaluate the distribution of output variation).

[0125] In the embodiment of the present application, the generated output characteristic evaluation results are three sets of output characteristic evaluation results, namely, the output characteristic evaluation results for wind power generation, the output characteristic evaluation results for photovoltaic power generation, and the output characteristic evaluation results for wind-solar complementary power generation; wherein each set of output characteristic evaluation results includes annual scale output characteristic evaluation results, monthly scale output characteristic evaluation results, daily scale output characteristic evaluation results, and hourly scale output characteristic evaluation results.

[0126] Compared with the prior art, the embodiment of the present application selects the year corresponding to the median annual full utilization hours from multiple years as a typical year, and generates annual scale output characteristic evaluation results, monthly scale output characteristic evaluation results, daily scale output characteristic evaluation results, and hourly scale output characteristic evaluation results according to the hourly output data of the typical year. By analyzing the wind and solar output characteristics in multiple time dimensions, the generated new energy output characteristics can reflect the differences and volatility characteristics of new energy output in different periods, thereby improving the applicability of the evaluation results of new energy power generation output characteristics.

[0127] In an embodiment of the present application, in order to further define and illustrate, Figure 2 As shown, according to the hourly output data of wind power or photovoltaic power or wind-solar complementary power in a typical year, a corresponding cumulative power generation-output coefficient-guarantee rate curve is generated, including:

[0128] 201. Arrange the hourly output data of typical years in descending order to obtain an hourly output data sequence.

[0129] The hourly output data is any one of wind power hourly output data, photovoltaic hourly output data, and wind-solar complementary hourly output data; the output data in the hourly output data sequence are arranged in descending order.

[0130] 202. Based on the stratified interval power calculation formula, the stratified interval power corresponding to the sequence is calculated according to the hourly output data of each sequence in the hourly output data sequence.

[0131] Specifically, the calculation formula for the stratified interval electricity can be expressed as the following formula:

[0132] E Bt =F t ×(P Dt -P Dt-1 )×8760

[0133] Among them, E Bt Indicates the t-th order of the stratified interval electricity, F t Indicates the frequency guarantee rate corresponding to the rated output of the t-th order, P Dt Indicates the output of the t-th order, P Dt-1 Indicates the output of the t-1th order.

[0134] 203. Take each sequence as the target sequence one by one, accumulate the stratified interval power of the target sequence and the stratified interval power of all sequences before the target sequence, obtain the cumulative power of the target sequence, and obtain the cumulative power of each sequence.

[0135] Exemplarily, the accumulated electricity of the first rank is the electricity of the first rank stratified interval; the accumulated electricity of the second rank is the sum of the electricity of the first rank stratified interval and the electricity of the second rank stratified interval; the accumulated electricity of the third rank is the sum of the electricity of the first rank stratified interval, the electricity of the second rank stratified interval, and the electricity of the third rank stratified interval, and so on, to obtain the accumulated electricity of each rank.

[0136] 204. Based on the frequency guarantee rate calculation formula, the frequency guarantee rates corresponding to the respective rank are calculated respectively.

[0137] Exemplarily, the frequency guarantee rate of the first rank is 1 / (8760+1); the frequency guarantee rate of the second rank is 2 / (8760+1); the frequency guarantee rate of the third rank is 3 / (8760+1), and so on, to obtain the frequency guarantee rate corresponding to each rank.

[0138] 205. Generate a cumulative power generation-output coefficient-guarantee rate curve chart based on the cumulative power generation, hourly output data, and frequency guarantee rate of each rank.

[0139] Among them, the cumulative power generation-output coefficient-guarantee rate curve is as follows: Figure 3 As shown in the figure, the curve that gradually decreases from left to right represents the curve corresponding to the output coefficient, and the curve that gradually increases from left to right represents the curve corresponding to the annual power generation utilization hours.

[0140] In an embodiment of the present application, in order to further define and illustrate, Figure 4 As shown, according to the hourly output data of wind power or photovoltaic power or wind-solar complementary power in typical years, the output coefficient change curves under different guarantee rates are generated, including:

[0141] 301. Divide the hourly output data according to the number of hours in a day to obtain multiple groups of daily hourly output data groups.

[0142] In this embodiment of the present application, the hourly output data of a typical year is divided into a group of 24 hours, and 365 groups of daily hourly output data groups can be obtained, that is, the hourly output data groups of each day in a typical year.

[0143] 302. From each daily hourly output data group, extract the hourly output data at the same time to form multiple groups of simultaneous hourly output data groups, and use each group of simultaneous hourly output data groups as a target simultaneous hourly output data group.

[0144] Exemplarily, the output data at time 0 in each daily hourly output data group is extracted to form the hourly output data group at time 0; the output data at time 1 in each daily hourly output data group is extracted to form the hourly output data group at time 1; the output data at time 2 in each daily hourly output data group is extracted to form the hourly output data group at time 2; and so on, the hourly output data group at time 0, the hourly output data group at time 1, the hourly output data group at time 2...23 hourly output data groups are obtained to provide 24 groups of hourly output data groups at the same time. It can be understood that each group of hourly output data groups at the same time contains 365 hourly output data. And the above 23 groups of hourly output data groups at the same time are used as target hourly output data groups at the same time one by one.

[0145] 303. Sort the hourly output data contained in the target moment hourly output data group to obtain a target moment hourly output data sequence, and determine the maximum output moment, average output moment, and minimum output moment of the target moment hourly output data group from the target moment hourly output data sequence.

[0146] Exemplarily, the 365 hourly output data in the hourly output data group at time 0 are sorted to obtain the hourly output data sequence at time 0, which can be in ascending or descending order, and is not specifically limited in the embodiments of the present application. Further, the hourly output data of a preset sequence are selected from the hourly output data sequence at time 0, such as, when arranged in descending order, the 18th sequence (365×5%) is selected as the output moment, the 183rd sequence (365×50%) is selected as the output average moment, and the 347th sequence (365×95%) is selected as the output moment; when arranged in ascending order, the 347th sequence (365×95%) is selected as the output moment, the 183rd sequence (365×50%) is selected as the output average moment, and the 18th sequence (365×5%) is selected as the output moment.

[0147] 304. Generate output coefficient variation curves under different guarantee rates according to the peak output moment, average output moment, and minimum output moment of each simultaneous hourly output data group.

[0148] In the embodiment of the present application, according to the method in step 303 of the embodiment, the maximum output moment, the average output moment, and the minimum output moment of each hourly output data group are determined, and a curve diagram of output coefficient change under different guarantee rates is generated, such as Figure 5 As shown in the figure, from top to bottom are the output coefficient change curve diagram at the time of large output, the output coefficient change curve diagram at the time of average output, and the output coefficient change curve diagram at the time of small output.

[0149] In an embodiment of the present application, in order to further define and illustrate, Figure 6As shown, according to the hourly output data of wind power or photovoltaic power or wind-solar complementary power in typical years, the corresponding monthly average output coefficient bar chart and monthly maximum output coefficient line chart are generated, including:

[0150] 401. Divide the hourly output data of a typical year by the number of hours in a month to obtain multiple groups of monthly hourly output data.

[0151] Among them, the number of hours per month is 24×30=720 hours. In the embodiment of the present application, the hourly output data of a typical year is divided into a group of 720 hours, and 12 groups of monthly hourly output data groups can be obtained, that is, the hourly output data groups of each month in a typical year.

[0152] 402. Calculate the monthly average output coefficient corresponding to each group of monthly hourly output data.

[0153] In the embodiments of the present application, the mean of each group of monthly hourly output data is calculated to obtain the monthly average output data, and then the monthly average output data is divided by the installed parameters to obtain the monthly average output coefficient for each month.

[0154] 403. Filter out the monthly maximum output data in each group of monthly hourly output data, and calculate the monthly maximum output coefficient corresponding to the monthly maximum output data.

[0155] In the embodiment of the present application, the monthly maximum output data is screened out from each group of monthly hourly output data, and then the monthly maximum output data is divided by the installed parameters to obtain the monthly maximum output coefficient.

[0156] 404. According to each monthly average output coefficient and the monthly maximum output coefficient, a monthly average output coefficient bar chart and a monthly maximum output coefficient line chart are generated.

[0157] Among them, the monthly average output coefficient bar chart and the monthly maximum output coefficient line chart are as follows: Figure 7 shown.

[0158] In an embodiment of the present application, in order to further define and illustrate, Figure 8 As shown, based on the hourly wind power output data or photovoltaic power output data or wind-solar complementary power output data in typical years, the corresponding monthly maximum and minimum continuous weekly hours power generation deviation rate line chart is generated, including:

[0159] 501. Each group of monthly hourly output data is used as the target monthly hourly output data group.

[0160] In the embodiment of the present application, each group of monthly hourly output data is taken as a target object one by one.

[0161] 502. Slidingly combine the target month hourly output data groups according to the weekly hours to obtain multiple groups of weekly hourly output data groups, calculate the weekly hourly power generation data groups corresponding to each group of weekly hourly output data groups, and sum the hourly power generation data in each group of weekly hourly power generation data groups to obtain the total power generation data for each weekly hour, and calculate the first mean of the total power generation data for each weekly hour.

[0162] Among them, the weekly hours are 24×7=168 hours; sliding combination, specifically, the 1st day to the 7th day is the 1st group of weekly hourly output data group, the 2nd day to the 8th day is the 2nd group of weekly hourly output data group, the 3rd day to the 9th day is the 3rd group of weekly hourly output data group... The 24th day to the 30th day is the 24th group of weekly hourly output data group. Further, the hourly output data in each group of weekly hourly output data group is multiplied by time to obtain the corresponding weekly hourly power generation data group. Then, the hourly power generation data in each group of weekly hourly power generation data group are summed up respectively to obtain multiple weekly hourly total power generation data, and the first mean of multiple weekly hourly total power generation data is calculated, wherein the first mean is used to characterize the mean of the weekly hourly total power generation data.

[0163] 503. Filter out the maximum weekly hourly total power generation data and the minimum weekly hourly total power generation data from the plurality of weekly hourly total power generation data, and calculate the first maximum deviation rate and the first minimum deviation rate according to the first mean value.

[0164] Among them, the first maximum deviation rate is used to characterize the deviation rate between the total power generation data of the maximum weekly hours and the first mean; the first minimum deviation rate is used to characterize the deviation rate between the total power generation data of the minimum weekly hours and the first mean.

[0165] 504. Generate a line graph of monthly maximum and minimum continuous weekly hours power generation deviation rate according to the first maximum deviation rate and the first minimum deviation rate corresponding to each target monthly hourly output data group.

[0166] Among them, the monthly maximum and minimum continuous weekly hours of power generation deviation rate line chart, such as Fig. 9 As shown in the figure, from top to bottom are the monthly maximum continuous weekly hours power generation deviation rate curve, and the monthly minimum continuous weekly hours power generation deviation rate curve.

[0167] In an embodiment of the present application, in order to further define and illustrate, Fig.10 As shown, according to the hourly output data of wind power or photovoltaic power or wind-solar complementary power in a typical year, a corresponding weekly power probability distribution curve is generated, including:

[0168] 601. Divide the hourly output data of a typical year by weekly hours to obtain multiple groups of weekly hourly output data, calculate the weekly hourly power generation data group corresponding to each group of weekly hourly output data, and sum the hourly power generation data in each group of weekly hourly power generation data to obtain multiple weekly total power generation data.

[0169] Among them, the number of hours per week is 24×7=168 hours. In the embodiment of the present application, the division is specific, the 1st day to the 7th day is the 1st group of weekly hourly output data group, the 8th day to the 14th day is the 2nd group of weekly hourly output data group, the 15th day to the 21st day is the 3rd group of weekly hourly output data group... The 358th day to the 364th day is the 52nd group of weekly hourly output data group. Further, the hourly output data in each group of weekly hourly output data group is multiplied by the time to obtain the corresponding weekly hourly power generation data group. Then, the hourly power generation data in each group of weekly hourly power generation data group is summed up to obtain multiple weekly total power generation data.

[0170] 602. Group multiple weekly total power generation data into first intervals according to a first preset grouping interval, and calculate a first probability density of each first grouping, so as to generate a weekly power generation probability distribution curve diagram according to the first probability density of each first grouping.

[0171] Specifically, the first preset group distance can be calculated according to the following formula:

[0172]

[0173] Wherein, GroupWidth represents the first preset group width, R represents the range of the weekly total power generation data, and N represents the number of weekly total power generation data.

[0174] The first probability density can be calculated according to the following formula,

[0175]

[0176] Among them, PDF represents the first probability density, σ represents the standard deviation of the weekly total power generation data, and E n-7d Represents the weekly total power generation data, E n-7d Represents the mean of the weekly total power generation data.

[0177] Weekly power probability distribution curve, such as Fig.11 shown.

[0178] In an embodiment of the present application, in order to further define and illustrate, Fig.12 As shown, based on the hourly wind power output data or photovoltaic hourly output data or wind-solar complementary hourly output data in typical years, the corresponding monthly maximum and minimum daily power generation deviation rate line chart is generated, including:

[0179] 701. Divide the hourly output data of a typical year by daily hours to obtain multiple groups of daily hourly output data groups.

[0180] In this embodiment of the present application, the hourly output data of a typical year is divided into a group of 24 hours, and 365 groups of daily hourly output data groups can be obtained, that is, the hourly output data groups of each day in a typical year.

[0181] 702. Calculate the daily hourly power generation data group corresponding to each group of daily hourly output data groups, sum the hourly power generation data in each group of daily hourly power generation data groups, obtain multiple daily total power generation data, and calculate the second mean of the multiple daily total power generation data.

[0182] Specifically, the hourly output data in each group of daily hourly output data is multiplied by time to obtain the corresponding daily hourly power generation data group. Then, the hourly power generation data in each group of daily hourly power generation data group are summed up to obtain a plurality of daily total power generation data, and the second mean of the plurality of daily total power generation data is calculated, wherein the second mean is used to represent the mean of the daily total power generation data.

[0183] 703. Divide the plurality of daily total power generation data by month to obtain a plurality of monthly daily total power generation data groups, and use each of the monthly daily total power generation data groups as a target monthly daily total power generation data group.

[0184] In the embodiment of the present application, the 356 daily total power generation data are divided by month, and 12 groups of monthly daily total power generation data groups can be obtained.

[0185] 704. Filter out the maximum daily total power generation data and the minimum daily total power generation data from the target month daily power generation data group, and calculate the second maximum deviation rate and the second minimum deviation rate according to the second mean value.

[0186] The second maximum deviation rate is used to characterize the deviation rate between the maximum daily total power generation data and the second mean; the second minimum deviation rate is used to characterize the deviation rate between the minimum daily total power generation data and the second mean.

[0187] 705. Generate a line graph of the monthly maximum and minimum daily power generation deviation rates according to the second maximum deviation rate and the second minimum deviation rate corresponding to each target monthly daily power generation data group.

[0188] Among them, the monthly maximum and minimum daily power generation deviation rate line chart is as follows: Fig.13 As shown in the figure, from top to bottom are the monthly maximum daily power generation deviation rate curve and the monthly minimum daily power generation deviation rate curve.

[0189] In one embodiment of the present application, in order to further limit and illustrate, based on the hourly wind power output data or photovoltaic hourly output data or wind-solar complementary hourly output data of typical years, a corresponding daily electricity quantity probability distribution curve is generated, including: grouping the total daily power generation data into second intervals according to a second preset group interval, and calculating the second probability density of each second grouping, so as to generate a daily electricity quantity probability distribution curve according to the second probability density of each second grouping.

[0190] The calculation formula of the second preset group distance is the same as the calculation formula of the first preset group distance, and the calculation formula of the second probability density is the same as the calculation formula of the first probability density, which will not be repeated here. Fig.14 shown.

[0191] In one embodiment of the present application, in order to further limit and illustrate, a corresponding hourly output amplitude variation line graph is generated based on the hourly wind power output data or photovoltaic hourly output data or wind-solar complementary hourly output data of typical years, including: calculating the hourly output amplitude variation data based on the hourly output data of typical years; dividing the hourly output amplitude variation data into intervals according to a preset interval ratio to obtain the hourly output amplitude variation line graph.

[0192] Among them, the hourly output amplitude data is the difference between the hourly output data at the current moment and the hourly output data at the previous moment; the preset interval ratio can be set to 0.1. Fig.15 shown.

[0193] In an embodiment of the present application, in order to further limit and illustrate, before obtaining the hourly wind power output data, photovoltaic hourly output data, and wind-solar complementary hourly output data of the target power station in multiple years, the embodiment method also includes: obtaining the hourly wind speed data and hourly solar radiation data of the area where the target power station is located in multiple years, and calculating the hourly wind power output data, photovoltaic hourly output data, and wind-solar complementary hourly output data based on the wind speed and wind power output conversion formula, and the solar radiation and photovoltaic power generation output conversion formula.

[0194] Specifically, the formula for converting wind speed to wind power output is expressed as follows:

[0195]

[0196] Among them, P t wt (v t ) represents the wind power output data at time t, v t represents the wind speed data at time t, v ci Indicates the fan cut-in wind speed, v co Indicates the fan cut-out wind speed, v rIndicates the wind speed corresponding to the rated power of the fan, P t wt Indicates the rated power of the fan unit.

[0197] The conversion formula between solar radiation and photovoltaic power generation output is expressed as the following formula:

[0198]

[0199] Among them, P(t) represents the photovoltaic output data at time t, P sn represents the rated power of the photovoltaic panel, I(t) represents the solar radiation data at time t, and I std Indicates the light intensity per unit area under standard conditions, R c represents the set specific intensity of light, and η represents the photoelectric conversion efficiency of the photovoltaic panel.

[0200] The present application provides an evaluation method for the output characteristics of renewable energy power generation. First, the hourly wind power output data, the hourly photovoltaic output data, and the hourly wind-solar complementarity output data of the target power station in multiple years are obtained, and each year is used as the target year one by one; secondly, according to the hourly wind power output data, the hourly photovoltaic output data, and the hourly wind-solar complementarity output data of the target year, the annual full generation utilization hours of wind power, the annual full generation utilization hours of photovoltaic power, and the annual full generation utilization hours of wind-solar complementarity are calculated respectively; thirdly, the annual full generation utilization hours of wind power, the annual full generation utilization hours of photovoltaic power, and the annual full generation utilization hours of wind-solar complementarity of each year are sorted respectively, and the median rank in the sequence is selected. The corresponding year is taken as the typical year; finally, based on the hourly wind power output data, photovoltaic hourly output data, and wind-solar complementary hourly output data of the typical year, the annual-scale output characteristic assessment results, monthly-scale output characteristic assessment results, daily-scale output characteristic assessment results, and hourly-scale output characteristic assessment results for wind power generation are generated respectively; the annual-scale output characteristic assessment results, monthly-scale output characteristic assessment results, daily-scale output characteristic assessment results, and hourly-scale output characteristic assessment results for photovoltaic power generation; the annual-scale output characteristic assessment results, monthly-scale output characteristic assessment results, daily-scale output characteristic assessment results, and hourly-scale output characteristic assessment results for wind-solar complementary power generation are generated respectively. Compared with the prior art, the embodiment of the present application selects the year corresponding to the median annual full utilization hours from multiple years as a typical year, and generates annual scale output characteristic evaluation results, monthly scale output characteristic evaluation results, daily scale output characteristic evaluation results, and hourly scale output characteristic evaluation results according to the hourly output data of the typical year. By analyzing the wind and solar output characteristics in multiple time dimensions, the generated new energy output characteristics can reflect the differences and volatility characteristics of new energy output in different periods, thereby improving the applicability of the evaluation results of new energy power generation output characteristics.

[0201] Furthermore, as a response to the above Figure 1 The implementation of the method shown in the embodiment of the present application provides an evaluation device for the output characteristics of new energy power generation, such as Fig.16 As shown, the device comprises:

[0202] Data acquisition module 81, annual full power utilization hours calculation module 82, typical year determination module 83, output characteristic evaluation result generation module 84;

[0203] The data acquisition module 81 is used to acquire the hourly output data of wind power, photovoltaic power and wind-solar hybrid power of the target power station in multiple years, and use each year as the target year.

[0204] The annual full generation utilization hours calculation module 82 is used to calculate the annual full generation utilization hours of wind power, the annual full generation utilization hours of photovoltaic power, and the annual full generation utilization hours of wind and solar complementarity according to the hourly output data of wind power, the hourly output data of photovoltaic power, and the hourly output data of wind and solar complementarity in the target year;

[0205] The typical year determination module 83 is used to sort the annual full utilization hours of wind power, the annual full utilization hours of photovoltaic power, and the annual full utilization hours of wind-solar complementarity in each year, and select the year corresponding to the median rank in the sequence as the typical year;

[0206] The output characteristic evaluation result generation module 84 is used to generate the annual-scale output characteristic evaluation result, monthly-scale output characteristic evaluation result, daily-scale output characteristic evaluation result and hourly-scale output characteristic evaluation result for wind power generation, the annual-scale output characteristic evaluation result, monthly-scale output characteristic evaluation result, daily-scale output characteristic evaluation result and hourly-scale output characteristic evaluation result for photovoltaic power generation, and the annual-scale output characteristic evaluation result, monthly-scale output characteristic evaluation result, daily-scale output characteristic evaluation result and hourly-scale output characteristic evaluation result for wind-solar complementary power generation according to the hourly wind power output data, hourly photovoltaic output data and hourly wind-solar complementary output data of the typical year.

[0207] In a specific application scenario, the annual-scale output characteristic evaluation results include a cumulative power generation-output coefficient-guarantee rate curve chart, and a curve chart of output coefficient changes under different guarantee rates; the monthly-scale output characteristic evaluation results include a monthly average output coefficient bar chart and a monthly maximum output coefficient line chart, a monthly maximum and minimum continuous weekly hours power generation deviation rate line chart, and a weekly power quantity probability distribution curve chart; the daily-scale output characteristic evaluation results include a monthly maximum and minimum daily power generation deviation rate line chart, and a daily power quantity probability distribution curve chart; the hourly-scale output characteristic evaluation results include an hourly output variation line chart.

[0208] In a specific application scenario, the output characteristic evaluation result generation module is used to:

[0209] Arrange the hourly output data of the typical year in descending order to obtain an hourly output data sequence, wherein the hourly output data is any one of wind power hourly output data, photovoltaic hourly output data, and wind-solar complementary hourly output data;

[0210] Based on the stratified interval power calculation formula, the stratified interval power corresponding to the sequence is calculated according to the hourly output data of each sequence in the hourly output data sequence;

[0211] Taking each sequence as the target sequence one by one, accumulating the stratified interval power of the target sequence and the stratified interval power of all sequences before the target sequence to obtain the cumulative power of the target sequence and the cumulative power of each sequence;

[0212] Based on the frequency guarantee rate calculation formula, the frequency guarantee rate corresponding to each rank is calculated respectively;

[0213] Generate a cumulative power generation-output coefficient-guarantee rate curve chart based on the cumulative power generation, hourly output data, and frequency guarantee rate of each rank;

[0214] And, dividing the hourly output data according to the number of hours in a day to obtain multiple groups of daily hourly output data groups;

[0215] Extracting the hourly output data at the same time from each of the daily hourly output data groups to form multiple groups of hourly output data at the same time, and taking each group of hourly output data at the same time as the target hourly output data at the same time;

[0216] Sorting the hourly output data contained in the target moment hourly output data group to obtain a target moment hourly output data sequence, and determining the maximum output moment, average output moment, and minimum output moment of the target moment hourly output data group from the target moment hourly output data sequence;

[0217] According to the peak output moment, average output moment and minimum output moment of each hourly output data group at the same time, a curve diagram of output coefficient variation under different guarantee rates is generated.

[0218] In a specific application scenario, the output characteristic evaluation result generation module is used to:

[0219] Dividing the hourly output data of the typical year according to the number of hours in a month to obtain multiple groups of monthly hourly output data;

[0220] Calculate the monthly average output coefficient corresponding to each group of monthly hourly output data respectively;

[0221] Respectively select the monthly maximum output data in each group of the monthly hourly output data, and calculate the monthly maximum output coefficient corresponding to the monthly maximum output data;

[0222] Generate a monthly average output coefficient bar graph and a monthly maximum output coefficient line graph according to each of the monthly average output coefficients and the monthly maximum output coefficients;

[0223] and, taking each of the monthly hourly output data groups as the target monthly hourly output data group;

[0224] Slidingly combining the target month hourly output data group according to the weekly hours to obtain multiple groups of weekly hourly output data groups, calculating the weekly hourly power generation data group corresponding to each group of the weekly hourly output data group, and summing the hourly power generation data in each group of the weekly hourly power generation data group to obtain multiple weekly hourly total power generation data, and calculating the first mean of the multiple weekly hourly total power generation data;

[0225] Filter out the maximum weekly hourly total power generation data and the minimum weekly hourly total power generation data from the plurality of weekly hourly total power generation data, and calculate the first maximum deviation rate and the first minimum deviation rate according to the first mean value;

[0226] Generate a line graph of monthly maximum and minimum continuous weekly hours power generation deviation rate according to the first maximum deviation rate and the first minimum deviation rate corresponding to each target monthly hourly output data group;

[0227] and dividing the hourly output data of the typical year according to the number of weekly hours to obtain multiple groups of weekly hourly output data, calculating the weekly hourly power generation data group corresponding to each group of the weekly hourly output data group, and summing the hourly power generation data in each group of the weekly hourly power generation data group to obtain multiple weekly total power generation data;

[0228] The plurality of weekly total power generation data are grouped into first intervals according to a first preset grouping distance, and a first probability density of each first group is calculated, so as to generate a weekly power generation probability distribution curve diagram according to the first probability density of each first group.

[0229] In a specific application scenario, the output characteristic evaluation result generation module is used to:

[0230] Dividing the hourly output data of the typical year according to the number of hours in a day to obtain multiple groups of daily hourly output data groups;

[0231] Calculating the daily hourly power generation data group corresponding to each group of the daily hourly output data group, and summing the hourly power generation data in each group of the daily hourly power generation data group to obtain a plurality of daily total power generation data, and calculating a second mean value of the plurality of daily total power generation data;

[0232] Divide the multiple daily total power generation data by month to obtain multiple groups of monthly daily total power generation data, and use each group of monthly daily total power generation data as a target monthly daily total power generation data group;

[0233] Filtering out the maximum daily total power generation data and the minimum daily total power generation data from the target month daily power generation data group, and calculating the second maximum deviation rate and the second minimum deviation rate according to the second mean value;

[0234] Generate a line graph of the monthly maximum and minimum daily power generation deviation rates according to the second maximum deviation rate and the second minimum deviation rate corresponding to each target monthly daily power generation data group;

[0235] Furthermore, the daily total power generation data are grouped into second intervals according to a second preset grouping distance, and the second probability density of each second group is calculated, so as to generate a daily power probability distribution curve diagram according to the second probability density of each second group.

[0236] In a specific application scenario, the output characteristic evaluation result generation module is used to:

[0237] Based on the hourly output data of the typical year, calculate the hourly output amplitude data;

[0238] The hourly output amplitude variation data is divided into intervals according to a preset interval ratio to obtain an hourly output amplitude variation line graph.

[0239] In a specific application scenario, before the data acquisition module, the device further includes:

[0240] The output data conversion module is used to obtain the hourly wind speed data and hourly solar radiation data of the target power station area in multiple years, and calculate the hourly wind power output data, photovoltaic hourly output data, and wind-solar complementary hourly output data based on the wind speed and wind power output conversion formula and the solar radiation and photovoltaic power output conversion formula.

[0241] The present application provides an evaluation device for the output characteristics of renewable energy power generation. First, the hourly wind power output data, the hourly photovoltaic output data, and the hourly wind-solar complementarity output data of the target power station in multiple years are obtained, and each year is used as the target year one by one; secondly, according to the hourly wind power output data, the hourly photovoltaic output data, and the hourly wind-solar complementarity output data of the target year, the annual full generation utilization hours of wind power, the annual full generation utilization hours of photovoltaic power, and the annual full generation utilization hours of wind-solar complementarity are calculated respectively; thirdly, the annual full generation utilization hours of wind power, the annual full generation utilization hours of photovoltaic power, and the annual full generation utilization hours of wind-solar complementarity of each year are sorted respectively, and the corresponding median rank in the sequence is selected. The corresponding year is taken as the typical year; finally, based on the hourly wind power output data, photovoltaic hourly output data, and wind-solar complementary hourly output data of the typical year, the annual-scale output characteristic assessment results, monthly-scale output characteristic assessment results, daily-scale output characteristic assessment results, and hourly-scale output characteristic assessment results for wind power generation are generated respectively; the annual-scale output characteristic assessment results, monthly-scale output characteristic assessment results, daily-scale output characteristic assessment results, and hourly-scale output characteristic assessment results for photovoltaic power generation; the annual-scale output characteristic assessment results, monthly-scale output characteristic assessment results, daily-scale output characteristic assessment results, and hourly-scale output characteristic assessment results for wind-solar complementary power generation are generated respectively. Compared with the prior art, the embodiment of the present application selects the year corresponding to the median annual full utilization hours from multiple years as a typical year, and generates annual scale output characteristic evaluation results, monthly scale output characteristic evaluation results, daily scale output characteristic evaluation results, and hourly scale output characteristic evaluation results according to the hourly output data of the typical year. By analyzing the wind and solar output characteristics in multiple time dimensions, the generated new energy output characteristics can reflect the differences and volatility characteristics of new energy output in different periods, thereby improving the applicability of the evaluation results of new energy power generation output characteristics.

[0242] According to an embodiment of the present application, a storage medium is provided, wherein the storage medium stores at least one executable instruction, and the computer executable instruction can execute the method for evaluating the output characteristics of renewable energy power generation in any of the above method embodiments.

[0243] Based on this understanding, the technical solution of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each implementation scenario of the present application.

[0244] Fig.17 A schematic diagram of the structure of a terminal provided according to an embodiment of the present application is shown, and the specific embodiment of the present application does not limit the specific implementation of the terminal.

[0245] like Fig.17 As shown, the terminal may include: a processor (processor) 902 , a communication interface (Communications Interface) 904 , a memory (memory) 906 , and a communication bus 908 .

[0246] The processor 902 , the communication interface 904 , and the memory 906 communicate with each other via a communication bus 908 .

[0247] The communication interface 904 is used to communicate with other devices such as clients or other servers.

[0248] The processor 902 is used to execute the program 910, and specifically can execute the relevant steps in the above-mentioned embodiment of the method for evaluating the output characteristics of new energy power generation.

[0249] Specifically, the program 910 may include program codes, which include computer operation instructions.

[0250] The processor 902 may be a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application. The one or more processors included in the computer device may be processors of the same type, such as one or more CPUs; or may be processors of different types, such as one or more CPUs and one or more ASICs.

[0251] The memory 906 is used to store the program 910. The memory 906 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0252] The program 910 may be specifically configured to enable the processor 902 to perform the following operations:

[0253] Obtain the hourly wind power output data, photovoltaic power output data, and wind-solar hybrid power output data of the target power station in multiple years, and use each year as the target year;

[0254] According to the hourly output data of wind power, photovoltaic power and wind-solar complementarity in the target year, calculate the annual full utilization hours of wind power, the annual full utilization hours of photovoltaic power and the annual full utilization hours of wind-solar complementarity respectively;

[0255] The annual full utilization hours of wind power, photovoltaic power, and wind-solar complementarity in each year are sorted separately, and the year corresponding to the median rank in the sequence is selected as the typical year;

[0256] According to the hourly output data of wind power, the hourly output data of photovoltaic power, and the hourly output data of wind-solar complementary power in the typical years, the annual-scale output characteristic assessment results, monthly-scale output characteristic assessment results, daily-scale output characteristic assessment results, and hourly-scale output characteristic assessment results for wind power generation are generated respectively; the annual-scale output characteristic assessment results, monthly-scale output characteristic assessment results, daily-scale output characteristic assessment results, and hourly-scale output characteristic assessment results for photovoltaic power generation; the annual-scale output characteristic assessment results, monthly-scale output characteristic assessment results, daily-scale output characteristic assessment results, and hourly-scale output characteristic assessment results for wind-solar complementary power generation are generated respectively.

[0257] The storage medium may also include an operating system and a network communication module. The operating system is a program that manages the hardware and software resources of the physical device of the above-mentioned method for evaluating the output characteristics of new energy power generation, and supports the operation of information processing programs and other software and / or programs. The network communication module is used to realize communication between components inside the storage medium, and communication with other hardware and software in the information processing physical device.

[0258] Each embodiment in this specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system embodiment, since it basically corresponds to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0259] The method and system of the present application may be implemented in many ways. For example, the method and system of the present application may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above order of steps for the method is for illustration only, and the steps of the method of the present application are not limited to the order specifically described above, unless otherwise specifically stated. In addition, in some embodiments, the present application may also be implemented as a program recorded in a recording medium, which includes machine-readable instructions for implementing the method according to the present application. Therefore, the present application also covers a recording medium storing a program for executing the method according to the present application.

[0260] Obviously, those skilled in the art should understand that the above modules or steps of the present application can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, and optionally, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order from that herein, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present application is not limited to any specific combination of hardware and software.

[0261] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for evaluating the output characteristics of renewable energy power generation, characterized in that: include: Obtain the hourly wind power output data, photovoltaic power output data, and wind-solar hybrid power output data of the target power station in multiple years, and use each year as the target year; According to the hourly output data of wind power, photovoltaic power and wind-solar complementarity in the target year, calculate the annual full utilization hours of wind power, the annual full utilization hours of photovoltaic power and the annual full utilization hours of wind-solar complementarity respectively; The annual full utilization hours of wind power, photovoltaic power, and wind-solar complementarity in each year are sorted separately, and the year corresponding to the median rank in the sequence is selected as the typical year; According to the hourly output data of wind power, the hourly output data of photovoltaic power, and the hourly output data of wind-solar complementary power in the typical years, the annual-scale output characteristic assessment results, monthly-scale output characteristic assessment results, daily-scale output characteristic assessment results, and hourly-scale output characteristic assessment results for wind power generation are generated respectively; the annual-scale output characteristic assessment results, monthly-scale output characteristic assessment results, daily-scale output characteristic assessment results, and hourly-scale output characteristic assessment results for photovoltaic power generation; the annual-scale output characteristic assessment results, monthly-scale output characteristic assessment results, daily-scale output characteristic assessment results, and hourly-scale output characteristic assessment results for wind-solar complementary power generation are generated respectively.

2. The method according to claim 1, characterized in that: The annual scale output characteristic evaluation results include the cumulative power generation-output coefficient-guarantee rate curve chart, and the output coefficient change curve chart under different guarantee rates; the monthly scale output characteristic evaluation results include the monthly average output coefficient bar chart and the monthly maximum output coefficient line chart, the monthly maximum and minimum continuous weekly hours power generation deviation rate line chart, and the weekly power probability distribution curve chart; the daily scale output characteristic evaluation results include the monthly maximum and minimum daily power generation deviation rate line chart, and the daily power probability distribution curve chart; the hourly scale output characteristic evaluation results include the hourly output variation line chart.

3. The method according to claim 2, characterized in that The generating of the corresponding cumulative power generation-output coefficient-guarantee rate curve graph and the output coefficient change curve graph under different guarantee rates according to the hourly wind power output data or the hourly photovoltaic output data or the hourly wind-solar complementary output data of the typical year includes: Arrange the hourly output data of the typical year in descending order to obtain an hourly output data sequence, wherein the hourly output data is any one of wind power hourly output data, photovoltaic hourly output data, and wind-solar complementary hourly output data; Based on the stratified interval power calculation formula, the stratified interval power corresponding to the sequence is calculated according to the hourly output data of each sequence in the hourly output data sequence; Taking each sequence as the target sequence one by one, accumulating the stratified interval power of the target sequence and the stratified interval power of all sequences before the target sequence to obtain the cumulative power of the target sequence and the cumulative power of each sequence; Based on the frequency guarantee rate calculation formula, the frequency guarantee rate corresponding to each rank is calculated respectively; Generate a cumulative power generation-output coefficient-guarantee rate curve chart based on the cumulative power generation, hourly output data, and frequency guarantee rate of each rank; And, dividing the hourly output data according to the number of hours in a day to obtain multiple groups of daily hourly output data groups; Extracting the hourly output data at the same time from each of the daily hourly output data groups to form multiple groups of hourly output data at the same time, and taking each group of hourly output data at the same time as the target hourly output data at the same time; Sorting the hourly output data contained in the target moment hourly output data group to obtain a target moment hourly output data sequence, and determining the maximum output moment, average output moment, and minimum output moment of the target moment hourly output data group from the target moment hourly output data sequence; According to the peak output moment, average output moment and minimum output moment of each hourly output data group at the same time, a curve diagram of output coefficient variation under different guarantee rates is generated.

4. The method according to claim 2, characterized in that: The method generates the corresponding monthly average output coefficient bar graph and monthly maximum output coefficient line graph, monthly maximum and minimum continuous weekly hours power generation deviation rate line graph, and weekly power generation probability distribution curve graph according to the hourly wind power output data or photovoltaic hourly output data or wind-solar complementary hourly output data of the typical year, including: Dividing the hourly output data of the typical year according to the number of hours in a month to obtain multiple groups of monthly hourly output data; Calculate the monthly average output coefficient corresponding to each group of monthly hourly output data respectively; Respectively select the monthly maximum output data in each group of the monthly hourly output data, and calculate the monthly maximum output coefficient corresponding to the monthly maximum output data; Generate a monthly average output coefficient bar graph and a monthly maximum output coefficient line graph according to each of the monthly average output coefficients and the monthly maximum output coefficients; and, taking each of the monthly hourly output data groups as the target monthly hourly output data group; Slidingly combining the target month hourly output data group according to the weekly hours to obtain multiple groups of weekly hourly output data groups, calculating the weekly hourly power generation data group corresponding to each group of the weekly hourly output data group, and summing the hourly power generation data in each group of the weekly hourly power generation data group to obtain multiple weekly hourly total power generation data, and calculating the first mean of the multiple weekly hourly total power generation data; Filter out the maximum weekly hourly total power generation data and the minimum weekly hourly total power generation data from the plurality of weekly hourly total power generation data, and calculate the first maximum deviation rate and the first minimum deviation rate according to the first mean value; Generate a line graph of monthly maximum and minimum continuous weekly hours power generation deviation rate according to the first maximum deviation rate and the first minimum deviation rate corresponding to each target monthly hourly output data group; and dividing the hourly output data of the typical year according to the number of weekly hours to obtain multiple groups of weekly hourly output data, calculating the weekly hourly power generation data group corresponding to each group of the weekly hourly output data group, and summing the hourly power generation data in each group of the weekly hourly power generation data group to obtain multiple weekly total power generation data; The plurality of weekly total power generation data are grouped into first intervals according to a first preset grouping distance, and a first probability density of each first group is calculated, so as to generate a weekly power generation probability distribution curve diagram according to the first probability density of each first group.

5. The method according to claim 2, characterized in that: The generating of the corresponding monthly maximum and minimum daily power generation deviation rate line graph and daily power generation probability distribution curve graph according to the hourly wind power output data or photovoltaic hourly output data or wind-solar complementary hourly output data of the typical year includes: Dividing the hourly output data of the typical year according to the number of hours in a day to obtain multiple groups of daily hourly output data groups; Calculating the daily hourly power generation data group corresponding to each group of the daily hourly output data group, and summing the hourly power generation data in each group of the daily hourly power generation data group to obtain a plurality of daily total power generation data, and calculating a second mean value of the plurality of daily total power generation data; Divide the multiple daily total power generation data by month to obtain multiple groups of monthly daily total power generation data, and use each group of monthly daily total power generation data as a target monthly daily total power generation data group; Filtering out the maximum daily total power generation data and the minimum daily total power generation data from the target month daily power generation data group, and calculating the second maximum deviation rate and the second minimum deviation rate according to the second mean value; Generate a line graph of the monthly maximum and minimum daily power generation deviation rates according to the second maximum deviation rate and the second minimum deviation rate corresponding to each target monthly daily power generation data group; Furthermore, the daily total power generation data are grouped into second intervals according to a second preset grouping distance, and the second probability density of each second group is calculated, so as to generate a daily power probability distribution curve diagram according to the second probability density of each second group.

6. The method according to claim 2, characterized in that The generating of the corresponding hourly output amplitude line graph according to the hourly wind power output data or the hourly photovoltaic output data or the hourly wind-solar complementary output data of the typical year includes: Based on the hourly output data of the typical year, calculate the hourly output amplitude data; The hourly output amplitude variation data is divided into intervals according to a preset interval ratio to obtain an hourly output amplitude variation line graph.

7. The method according to claim 1, characterized in that Before obtaining the hourly wind power output data, the hourly photovoltaic output data, and the hourly wind-solar complementary output data of the target power station in multiple years, the method further includes: Obtain the hourly wind speed data and hourly solar radiation data of the target power station area in multiple years, and calculate the hourly wind power output data, photovoltaic hourly output data, and wind-solar complementary hourly output data based on the wind speed and wind power output conversion formula and the solar radiation and photovoltaic power output conversion formula.

8. A device for evaluating the output characteristics of renewable energy power generation, characterized in that: include: A data acquisition module is used to acquire the hourly output data of wind power, photovoltaic power, and wind-solar hybrid power of the target power station in multiple years, and use each year as the target year; The annual full generation utilization hours calculation module is used to calculate the annual full generation utilization hours of wind power, the annual full generation utilization hours of photovoltaic power, and the annual full generation utilization hours of wind-solar complementarity according to the hourly output data of wind power, the hourly output data of photovoltaic power, and the hourly output data of wind-solar complementarity in the target year; The typical year determination module is used to sort the annual full utilization hours of wind power, the annual full utilization hours of photovoltaic power, and the annual full utilization hours of wind-solar complementarity in each year, and select the year corresponding to the median rank in the sequence as the typical year; The output characteristic assessment result generation module is used to generate the annual-scale output characteristic assessment result, monthly-scale output characteristic assessment result, daily-scale output characteristic assessment result and hourly-scale output characteristic assessment result for wind power generation, the annual-scale output characteristic assessment result, monthly-scale output characteristic assessment result, daily-scale output characteristic assessment result and hourly-scale output characteristic assessment result for photovoltaic power generation, and the annual-scale output characteristic assessment result, monthly-scale output characteristic assessment result, daily-scale output characteristic assessment result and hourly-scale output characteristic assessment result for wind-solar complementary power generation according to the hourly wind power output data, hourly photovoltaic output data and hourly wind-solar complementary output data of the typical year.

9. A storage medium, wherein at least one executable instruction is stored in the storage medium, characterized in that: The executable instructions enable the processor to execute operations corresponding to the method for evaluating the output characteristics of renewable energy power generation as described in any one of claims 1-7.

10. A terminal, comprising: A processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus; The memory is used to store at least one executable instruction, wherein the executable instruction enables the processor to perform operations corresponding to the method for evaluating the output characteristics of new energy power generation as described in any one of claims 1-7.

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