CN116780643B - Confidence output calculation method and system for new energy participation in electric power balance - Google Patents
Confidence output calculation method and system for new energy participation in electric power balance Download PDFInfo
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Abstract
The invention provides a confidence output calculation method and a system for new energy participation in electric power balance, wherein the method comprises the following steps: acquiring the power load of the whole network and new energy output data, and calculating actual output coefficient sets of each new energy according to the new energy output data and the contemporaneous installed capacity; under the same season and same time period, screening a first sample and a second sample from the new energy actual output coefficient set according to a first mode and a second mode respectively, and calculating a first guarantee output coefficient and a second guarantee output coefficient of the first sample and the second sample under a preset confidence probability; the first mode is that a load threshold value is determined firstly, a sample day is screened out, and a period of time is screened out in the sample day; the second way is to determine a screening period and a load threshold; and calculating weighted values of the first guaranteed output coefficient and the second guaranteed output coefficient as a confidence output proportion. Based on the method, a confidence output computing system for the participation of the new energy in the electric power balance is also provided, and the method can provide specific guidance for how the new energy participates in the electric power balance.
Description
Technical Field
The invention belongs to the technical field of power supply, and particularly relates to a confidence output calculation method and system for new energy participation in power balance.
Background
In recent years, new energy sources such as wind power, photovoltaic power generation and the like are rapidly developed, the power generation output of the new energy sources is gradually increased in proportion to the whole network power load, but the power generation output of the new energy sources is strong in volatility and randomness, the stable power supply capacity cannot be formed under the prior art condition, and the new energy sources are influenced by characteristics such as no light in late peak and no wind in extreme heat, and the new energy sources have natural matching time difference between the power generation output of the new energy sources and the load peak, so that the new energy sources bring greater challenges to other units and the power grid adaptability. The photovoltaic output characteristic is in a semi-envelope shape, the maximum output in the noon can reach 70% -80% of the installed machine, but the photovoltaic output characteristic is greatly influenced by meteorological conditions, and the load supporting effect is greatly reduced in the cloudy day; the wind power output has stronger fluctuation, no obvious rule, larger influence by factors such as seasons and the like, and poorer reliability for supporting load peaks.
With the large-scale development of new energy, the principle of incorporating electric power balance is getting more and more attention, but the resource conditions of each place, the development of new energy and the like are greatly different, and the specific participation balance proportion is difficult to be agreed. GB/T38969-2020 "technical guidelines for electric power systems" indicates that the randomness and intermittence characteristics of the installation force of new energy should be fully considered, and the installation force of new energy is brought into electric power and electricity balance by combining with the prediction of the power of new energy and taking the principles of safety, reliability, economy and high efficiency as basic principles; the new energy power generation output is preferably participated in the power balance calculation in a proper proportion. However, in view of the differences between the actual situations of the various places, the guidance does not define specific proportions and specific calculation methods, and at present, researches on proper proportions and calculation methods for participating in power balance of new energy are not available. In the past, the influence of uncertainty of new energy output on supply safety is considered more, and the ratio of taking an empirical value and an extremely low output coefficient as new energy into electric power balance is often taken into consideration, but the deviation from the actual output level of the new energy is larger, and the requirement of the construction of a novel electric power system is also more and more difficult to meet.
Disclosure of Invention
In order to solve the technical problems, the invention provides a confidence output calculation method and a confidence output calculation system for new energy to participate in electric power balance, which comprehensively integrate the relations of safety, reliability, economy and high efficiency, can provide specific guidance for how the new energy participates in electric power balance, and provide an important basis for safe and reliable supply of electric power in a large-scale new energy access background.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
a confidence output calculation method for new energy participation in electric power balance comprises the following steps:
acquiring historical full-grid power load and new energy output data, and calculating actual output coefficient sets of each new energy according to the new energy output data and the contemporaneous installed capacity;
under the same season and the same period, a first sample and a second sample are respectively screened from the actual output coefficient set of the new energy according to a first mode and a second mode, and a first guarantee output coefficient of the first sample under the preset confidence probability is calculated; calculating a second guaranteed output coefficient of the second sample under the preset confidence probability; the first mode is that a load threshold value is firstly determined, a sample day is screened out, and a screening period is determined in the sample day; the second way is to determine a screening period and a load threshold;
and calculating a weighted average value of the first guaranteed output coefficient and the second guaranteed output coefficient as a confidence output proportion of the new energy participating in the electric power balance in the same season and the same period.
Further, the method for calculating the actual output coefficient set of each new energy according to the new energy output data and the contemporaneous installed capacity comprises the following steps:
determining wind power generation output data and photovoltaic power generation output data;
comparing each data of wind power generation output with synchronous installed capacity to obtain a wind power actual output coefficient set; and comparing each data of the photovoltaic power generation output with the synchronous installed capacity to obtain a photovoltaic actual output coefficient set.
Further, the specific process of obtaining the historical full-grid power load and the new energy output data and calculating the actual output coefficient set of each new energy according to the new energy output data and the contemporaneous installed capacity comprises the following steps:
full-network power load L= { L 1 ，L 2 ，…，L N -a }; wherein L is 1 The 1 st whole network electricity load data; l (L) 2 The 2 nd whole network electricity load data; l (L) N The power load data is used for the Nth whole network; n is the total number of data;
wind power output P W ＝{P W1 ，P W2 ，…，P WN -a }; wherein P is W1 The 1 st wind power output data; p (P) W2 The data is the 2 nd wind power output data; p (P) WN The Nth wind power output data;
photovoltaic output P S ＝{P S1 ，P S2 ，…，P SN -a }; wherein P is S1 Photovoltaic output data 1; p (P) S2 Photovoltaic output data 2; p (P) SN Is the Nth photovoltaic output data;
W n the actual output coefficient of the nth wind power is obtained; g Wn The synchronous wind power installation capacity corresponding to the nth wind power output is obtained;
S n the actual output coefficient of the nth photovoltaic is the actual output coefficient of the nth photovoltaic; g Sn And the synchronous photovoltaic installed capacity corresponding to the nth photovoltaic output is obtained.
Further, the process of screening the first sample from the actual output coefficient set of the new energy source in the same season and in the same period of time comprises the following steps:
calculating the ratio R= { R of the daily maximum load to the same-season maximum load 1 ,r 2 ,…，r D -a }; wherein r is d ∈(0,1]D=1, 2 … D, D is the total number of days of raw data;wherein L is dmax For maximum load on day d, L smax The same season maximum load on day d; r is (r) d The ratio of the maximum load on day d to the maximum load in the same season;
screening out sample days meeting a load threshold, screening out daily load peak time and daily load peak time according to the sample days, and respectively connecting the load peak time and the load peak time into a section as a first period of new energy confidence output analysis; and extracting a first sample of the load and the new energy output coefficient from the first period.
Further, the process of calculating the first guaranteed output coefficient of the first sample under the preset confidence probability includes:
after the first sample data are ordered from big to small, setting the preset confidence probability level as beta in the same season and in the same period of time; make i satisfyi is a natural number; the first sample ordering comprises: wind power generation coefficient set in first sample +.>Wherein (1)>The 1 st wind power output coefficient in the first sample; />The 2 nd wind power output coefficient in the first sample; />The X-th wind power output coefficient in the first sample; x is the number of the first samples after sequencing;
photovoltaic power generation output coefficient set in first sampleWherein (1)>The 1 st photovoltaic power generation output coefficient in the first sample; />The power coefficient of the 2 nd photovoltaic power generation in the first sample;the X-th photovoltaic power generation output coefficient in the first sample;
so the first guarantee output coefficient of wind power under the preset confidence probability beta isThe first guarantee output coefficient of photovoltaic power generation is +.>
Further, the process of screening the second sample according to the time interval includes:
combining local load characteristics of each season to determine the peak period and the late peak period of each season; carrying out per unit treatment on the peak-to-peak load according to the peak-to-peak maximum value in the same season; carrying out per unit treatment on the load in the late peak period according to the maximum value of the late peak in the same season; carrying out per unit treatment on the data of different years;
and screening a second sample meeting a load threshold from the load data subjected to per unit processing, and screening a second sample of the new energy output coefficient according to the second sample of the load.
Further, the process of calculating the second guaranteed output coefficient of the second sample under the preset confidence probability includes:
after the second sample data are ordered from big to small, setting the preset confidence probability level as beta in the same season and at the same time period; make j satisfyj is a natural number; the second sample ordering includes: wind power output coefficient set in second sample>Wherein (1)>The 1 st wind power output coefficient in the second sample; />The 2 nd wind power output coefficient in the second sample; />The Y-th wind power output coefficient in the second sample; y is the number of the second samples after sequencing; photovoltaic power generation output coefficient set in second sampleWherein (1)>For 1 st in the second sampleThe output coefficient of photovoltaic power generation; />The power coefficient of the 2 nd photovoltaic power generation in the second sample; />The power coefficient of the Y-th photovoltaic power generation in the second sample;
so the second guaranteed output coefficient of wind power under the preset confidence probability beta isThe second guaranteed output coefficient of the photovoltaic power generation is +.>
Further, the method further comprises:
under the same season and same period, screening out a first sample of the load, carrying out per unit processing according to the maximum value of the load point by point, respectively processing data of different years, calculating the variance of all points of the sample points of the load deviating from the maximum value of the load in the first sample, and marking as sigma 1 ；
After screening out the second load sample, carrying out per unit processing according to the maximum load point by point, and after processing the data of different years respectively, calculating the variance of all points of the load sample point deviating from the maximum load value in the second sample, and marking as sigma 2 。
Further, the process of calculating the weighted average of the first guaranteed output coefficient and the second guaranteed output coefficient as the confidence output proportion of the new energy to participate in the electric power balance includes:
under the same season and the same period, the proportion of wind power participating in power balance is as follows:
the proportion of the participation of the photovoltaic power generation in the power balance is as follows:
wherein,said mu 1 The weight of the first guaranteeing output coefficient; mu (mu) 2 And the weight of the second guaranteed output coefficient.
The invention also provides a confidence output calculation system for the new energy to participate in the electric power balance, which comprises an acquisition module, a first calculation module and a second calculation module;
the acquisition module is used for acquiring historical full-grid power load and new energy output data and calculating actual output coefficient sets of each new energy according to the new energy output data and the contemporaneous installed capacity;
the first calculation module is used for calculating a first sample and a second sample which are screened from the actual output coefficient set of the new energy source according to a first mode and a second mode respectively under the same season and the same period, and calculating a first guarantee output coefficient of the first sample under the preset confidence probability; calculating a second guaranteed output coefficient of the second sample under the preset confidence probability; the first mode is that a load threshold value is firstly determined, a sample day is screened out, and a screening period is determined in the sample day; the second way is to determine a screening period and a load threshold;
the second calculation module is used for calculating a weighted average value of the first guaranteed output coefficient and the second guaranteed output coefficient as a confidence output proportion of the new energy participating in the electric power balance in the same season and the same period.
The effects provided in the summary of the invention are merely effects of embodiments, not all effects of the invention, and one of the above technical solutions has the following advantages or beneficial effects:
the invention provides a confidence output calculation method and a system for new energy participation in electric power balance, wherein the method comprises the following steps: acquiring historical full-grid power load and new energy output data, and calculating actual output coefficient sets of each new energy according to the new energy output data and the contemporaneous installed capacity; under the same season and the same period, a first sample and a second sample are respectively screened from the actual output coefficient set of the new energy according to a first mode and a second mode, and a first guarantee output coefficient of the first sample under the preset confidence probability is calculated; calculating a second guaranteed output coefficient of the second sample under the preset confidence probability; firstly, determining a load threshold, screening out a sample day, and determining a screening period in the sample day; the second way is to determine a screening period and a load threshold; and calculating a weighted average value of the first guaranteed output coefficient and the second guaranteed output coefficient as a confidence output proportion of the new energy participating in the electric power balance in the same season and the same period. Based on a new energy participation power balance confidence output calculation method, a new energy participation power balance confidence output calculation system is also provided, the safe, reliable, economical and efficient relation is comprehensively arranged, specific guidance can be provided for how the new energy participates in power balance, and an important basis is provided for safe and reliable power supply under a large-scale new energy access background.
The invention takes the weighted average value of the guaranteed output coefficient under a certain load level and a certain confidence probability calculated by two methods as a specific proportion of new energy participating in the power balance of a certain period of a certain season, thereby improving the confidence output reliability.
Drawings
FIG. 1 is a flow chart of a method for calculating confidence output of new energy participating in electric power balance according to embodiment 1 of the present invention;
FIG. 2 is a flow chart of calculating the actual output coefficient of the new energy in embodiment 1 of the present invention;
FIG. 3 is a flowchart of the method for calculating the first guaranteed output coefficient according to embodiment 1 of the present invention;
FIG. 4 is a flowchart of the method for calculating the second guaranteed output coefficient according to embodiment 1 of the present invention;
fig. 5 is a schematic diagram of a confidence output computing system for new energy participation in electric power balance according to embodiment 2 of the present invention.
Detailed Description
In order to clearly illustrate the technical features of the present solution, the present invention will be described in detail below with reference to the following detailed description and the accompanying drawings. The following disclosure provides many different embodiments, or examples, for implementing different structures of the invention. In order to simplify the present disclosure, components and arrangements of specific examples are described below. Furthermore, the present invention may repeat reference numerals and/or letters in the various examples. This repetition is for the purpose of simplicity and clarity and does not in itself dictate a relationship between the various embodiments and/or configurations discussed. It should be noted that the components illustrated in the figures are not necessarily drawn to scale. Descriptions of well-known components and processing techniques and processes are omitted so as to not unnecessarily obscure the present invention.
Example 1
The embodiment 1 of the invention provides a confidence output calculation method for new energy to participate in electric power balance, which comprises the steps of firstly collecting output data of load, wind power and photovoltaic power generation in recent years, and carrying out per unit processing on the output data of the wind power and the photovoltaic power generation according to synchronous installed capacity. Based on the method, for a certain season and a certain period, the guaranteed output coefficient under a certain load level and a certain confidence probability is calculated by two methods respectively, and the weighted average value of the two methods is taken as a specific proportion of the new energy power generation participating in the power balance calculation in the season and the period, wherein the guaranteed output coefficient under the certain confidence probability is the confidence output coefficient.
FIG. 1 is a flow chart of a method for calculating confidence output of new energy participating in electric power balance;
in step S1, historical full-grid electricity load and new energy output data are obtained, and actual output coefficient sets of each new energy are calculated according to the new energy output data and the contemporaneous installed capacity.
FIG. 2 is a flow chart of calculating the actual output coefficient of the new energy in embodiment 1 of the present invention;
in step S11, data of power load, wind power and photovoltaic power generation output of the whole network, which are recorded for several years, are acquired, and the data time intervals are the same. Because the randomness of the power generation of the new energy is strong, the time interval of the original data cannot be too long, and the time interval is preferably less than 15 minutes.
Wherein, the whole network power consumption load L= { L 1 ，L 2 ，…，L N -a }; wherein L is 1 The 1 st whole network electricity load data; l (L) 2 The 2 nd whole network electricity load data; l (L) N The power load data is used for the Nth whole network; n is the total number of data;
wind power output P W ＝{P W1 ，P W2 ，…，P WN -a }; wherein P is W1 The 1 st wind power output data; p (P) W2 The data is the 2 nd wind power output data; p (P) WN The Nth wind power output data;
photovoltaic output P S ＝{P S1 ，P S2 ，…，P SN -a }; wherein P is S1 Photovoltaic output data 1; p (P) S2 Photovoltaic output data 2; p (P) SN Is the nth photovoltaic output data.
In step S12, comparing each data of wind power generation output with synchronous installed capacity to obtain a wind power actual output coefficient set; and comparing each data of the photovoltaic power generation output with the synchronous installed capacity to obtain a photovoltaic actual output coefficient set.
W n The actual output coefficient of the nth wind power is obtained; g Wn The synchronous wind power installation capacity corresponding to the nth wind power output is obtained;
S n the actual output coefficient of the nth photovoltaic is the actual output coefficient of the nth photovoltaic; g Sn And the synchronous photovoltaic installed capacity corresponding to the nth photovoltaic output is obtained.
In step S2, under the same season and the same period, a first sample and a second sample are screened out from the actual output coefficient set of the new energy source according to a first mode and a second mode respectively, and a first guarantee output coefficient of the first sample under a preset confidence probability is calculated; calculating a second guaranteed output coefficient of the second sample under the preset confidence probability; the first mode is that a load threshold value is firstly determined, a sample day is screened out, and a screening period is determined in the sample day; the second way is to determine a screening period and a load threshold;
in order to ensure the reliability of the calculation and analysis results, two methods are adopted for calculation respectively. FIG. 3 is a flowchart of the method for calculating the first guaranteed output coefficient according to embodiment 1 of the present invention;
in step S31, the ratio r= { R of the daily maximum load to the same-season maximum load is calculated 1 ,r 2 ,…，r D -a }; wherein r is d ∈(0,1]D=1, 2 … D, D is the total number of days of raw data;wherein L is dmax For maximum load on day d, L smax The same season maximum load on day d; r is (r) d The ratio of the maximum load on day d to the maximum load in the same season; respectively calculating different years;
screening out sample days meeting a load threshold, wherein the screened sample days meet r d Not less than a, wherein a is a load level threshold; a is more than or equal to 0 and less than or equal to 1; a is selected based on local load characteristics, peak load duration, etc. The day of the sample is the date of heavy load in the season, which can be guaranteed to be similar to the weather condition of the day of maximum load. If the load level is too low or too high as a whole in special weather, a can be adjusted up and down appropriately.
In step S32, screening out sample days meeting a load threshold, screening out daily load peak time and daily load peak time according to the sample days, and respectively connecting the load peak time and the load peak time into a section as a first period of new energy confidence output analysis; a first sample of the load and the new energy output coefficient is extracted from the first time period.
According to the screened sample days, the daily load noon and evening peak time (namely, the time corresponding to the noon peak maximum load and the evening peak maximum load) is counted, and the daily noon and evening peak time are respectively connected into a section and used as a time period for new energy confidence output analysis, namely, a new energy confidence output first time period.
Taking the peak in summer as an example, assuming that the screened sample days are K days, finding out the corresponding moment of the maximum load of the peak in summer every day to form a set T= { T 1 ,t 2 ，…，t K }. Wherein t is k K=1, 2, … K, which is the time corresponding to the peak maximum load at the kth day.
The peak noon time period is [ t ] min ,t max ]. Wherein t is min ＝min{t 1 ,t 2 ，…，t K }，t max ＝max{t 1 ,t 2 ，…，t K }。
In step S33, samples are extracted according to the day and time period of the screened samples, and after the samples are sequentially arranged from large to small, a first sample is obtained, and a first guarantee output coefficient of the first sample under a preset confidence probability is calculated.
After the first sample data are ordered from big to small, setting the preset confidence probability level as beta in the same season and in the same period of time; make i satisfyi is a natural number; the first sample ordering comprises:
wind power output coefficient set in first sampleWherein (1)>The 1 st wind power output coefficient in the first sample; />The 2 nd wind power output coefficient in the first sample; />The X-th wind power output coefficient in the first sample; x is the number of the first samples after sequencing;
photovoltaic in first samplePower generation output coefficient setWherein (1)>The 1 st photovoltaic power generation output coefficient in the first sample; />The power coefficient of the 2 nd photovoltaic power generation in the first sample;the X-th photovoltaic power generation output coefficient in the first sample;
so the first guarantee output coefficient of wind power under the preset confidence probability beta isThe first guarantee output coefficient of photovoltaic power generation is +.>
Beta is generally about 95% in terms of safety and economy.
FIG. 4 is a flowchart of the method for calculating the second guaranteed output coefficient according to embodiment 1 of the present invention;
in step S41, the peak period and the peak period of each season are determined in combination with the local load characteristics of each season; carrying out per unit treatment on the peak-at-noon period according to the peak-at-noon maximum value in the same season; carrying out per unit treatment on the late peak time period according to the maximum value of the late peak in the same season; carrying out per unit treatment on the data of different years;
rough division of late noon peaks: and (5) roughly dividing the time periods of each season in the morning and evening by integrating the load characteristics and the sunshine time of each local season.
In step S42, a second sample satisfying the load threshold is selected from the load data after the per unit processing, and a second sample of the new energy output coefficient is selected according to the load second sample.
The load data in the noon and evening peak periods are subjected to per unit treatment according to the noon and evening peak maximum values in the same season (the different years are treated respectively, but attention is paid to that 12 months in the current year and 1 month in the next year belong to the same season).
In step S43, a second guaranteed output coefficient of the second sample at a preset confidence probability is calculated. Mapping the date and time of the load point value which is greater than or equal to a to new energy output data, screening out new energy output coefficient sample points, obtaining a second sample after arranging from large to small, and calculating a guaranteed output coefficient under a certain confidence probability.
Taking summer peak as an example, the wind power output coefficient set in the second sample Wherein (1)>The 1 st wind power output coefficient in the second sample; />The 2 nd wind power output coefficient in the second sample; />The Y-th wind power output coefficient in the second sample; y is the number of the second samples after sequencing; photovoltaic power generation output coefficient set in second sample +.>Wherein (1)>The 1 st photovoltaic power generation output coefficient in the second sample; />For the 2 nd photovoltaic power generation output system in the second sampleA number; />The power coefficient of the Y-th photovoltaic power generation in the second sample;
so the second guaranteed output coefficient of wind power under the preset confidence probability beta isThe second guaranteed output coefficient of the photovoltaic power generation is +.>
In step S3, a weighted average of the first guaranteed output coefficient and the second guaranteed output coefficient is calculated as a confidence output ratio of the new energy to participate in the electric power balance.
The power balance calculation mainly checks the power supply situation during the load peak period, the new energy power generation participates in the power balance calculation, and the relations of safety, reliability, economy and efficiency are required to be comprehensively prepared, so that the low output condition under the severe weather condition during the load peak period is fully considered, the output coefficient cannot be too low, and the conditions of repeated construction of a loader, rising of the power rejection rate and the like are avoided. Comprehensively considering, aiming at a certain period of a certain season, taking a weighted average value of a certain load level and a certain confidence probability, which are calculated by two methods, of a guaranteed output coefficient as a specific proportion of new energy to participate in the power balance of the period.
Under the same season and same period, screening out a first sample of the load, carrying out per unit processing according to the maximum value of the load point by point, respectively processing data of different years, calculating the variance of all points of the sample points of the load deviating from the maximum value of the load in the first sample, and marking as sigma 1 ；
After screening out the second load sample, carrying out per unit processing according to the maximum load point by point, and after processing the data of different years respectively, calculating the variance of all points of the load sample point deviating from the maximum load value in the second sample, and marking as sigma 2 。
Under the same season and the same period, the proportion of wind power participating in power balance is as follows:
the proportion of the participation of the photovoltaic power generation in the power balance is as follows:
wherein,said mu 1 The weight of the first guaranteeing output coefficient; mu (mu) 2 And the weight of the second guaranteed output coefficient.
The present invention will be described in terms of specific examples to illustrate the process of implementing the invention, and the scope of the invention is not limited to the data in the examples.
And collecting the whole-grid power load, wind power and photovoltaic power generation output data and synchronous installed capacity of 5 min-level of a certain provincial power grid 2019-2022, and calculating to obtain the output coefficients of the wind power and photovoltaic power generation 5 min-level. Because the power grid electricity load has characteristics of summer and winter double peak in the year and noon and evening double peak in the day, the power supply is relatively tension, and therefore, analysis is carried out on four typical scenes of summer, winter load noon and evening peak.
Comprehensively considering the load characteristics of the power grid, peak load duration and the like, and taking a load level screening threshold value a as 95%.
According to the first method: and (5) screening out summer and winter sample days according to the 95% load level, respectively counting the corresponding moments of the peak maximum load and the peak maximum load of each day, and determining the peak distribution time period.
Table one: 2019-2022 summer and winter load noon, late peak hours (95% load level)
Screening out a first sample of the new energy output coefficient based on the selected sample day and time period, and ensuring the output coefficient of wind and light under the 95% confidence probability level as shown in the following table 2:
table 2: guaranteed output coefficient at 95% confidence probability
According to the second method: the load characteristic of the power grid and the local sunlight condition are comprehensively considered, and the noon and evening peak time periods are roughly divided.
Table 3: coarse division of 2019-2022 summer and winter load in the morning and evening peak hours
| Peak load time | Load late peak time | |
| (Summer) | 10:00-15:30 | 19:00-22:00 |
| Winter season | 8:00-15:30 | 16:30-22:00 |
And carrying out per unit processing on the load data of the noon and evening peak time periods according to the noon and evening peak maximum values in the same season, and screening a second sample of the new energy output coefficient according to the date and time of which 95% or more of load point values belong. At a 95% confidence level, the wind and solar guaranteed output coefficients are shown in Table 4 below.
Table 4: guaranteed output coefficient at 95% confidence probability
Taking the summer peak as an example, the weight mu of the first method 1 Weight μ of method two of 0.558 2 0.442, the proportion of wind power participating in power balance in the peak summer period is as follows: 0.558 x 1.5% +0.442 x 1% = 1.3%; the proportion of the participation of the photovoltaic power generation in the power balance is as follows: 0.558 x 28.9% +0.442 x 27.2% = 28.1%.
The calculation results of the proportion of the new energy power generation participating in the power balance in other typical scenes are shown in the following table 5:
table 5: proportion of new energy to participate in electric power balance
| Scene(s) | Summer peak at noon | Summer late peak | Peak in winter | Winter late peak |
| Wind power generation | 1.3％ | 5.6％ | 0.7％ | 1.3％ |
| Photovoltaic device | 28.1％ | 0.0％ | 1.6％ | 0.0％ |
The confidence output calculation method for the participation of the new energy in the electric power balance provided by the embodiment 1 of the invention integrates the safe, reliable, economical and efficient relations, can provide specific guidance for how the new energy participates in the electric power balance, and provides an important basis for the safe and reliable supply of the electric power in the large-scale new energy access background.
According to the confidence output calculation method for the new energy to participate in the power balance provided by the embodiment 1 of the invention, aiming at a certain period of a certain season, a weighted average value of a certain load level calculated by the two methods and a certain confidence probability under a certain guaranteed output coefficient is taken as a specific proportion of the new energy to participate in the power balance of the period of the season, so that the confidence output reliability is improved.
Example 2
Based on the confidence output calculation method of the new energy participation power balance provided in the embodiment 1 of the present invention, the embodiment 2 of the present invention further provides a confidence output calculation system of the new energy participation power balance, as shown in fig. 5, which is a schematic diagram of the confidence output calculation system of the new energy participation power balance in the embodiment 2 of the present invention. The system comprises an acquisition module, a first calculation module and a second calculation module;
the acquisition module is used for acquiring historical full-grid power load and new energy output data and calculating actual output coefficient sets of each new energy according to the new energy output data and the contemporaneous installed capacity;
the first calculation module is used for calculating a first sample and a second sample which are screened from the actual output coefficient set of the new energy source according to a first mode and a second mode respectively under the same season and the same period, and calculating a first guarantee output coefficient of the first sample under the preset confidence probability; calculating a second guaranteed output coefficient of the second sample under the preset confidence probability; the first mode is that a load threshold value is firstly determined, a sample day is screened out, and a screening period is determined in the sample day; the second way is to determine a screening period and a load threshold;
the second calculation module is used for calculating a weighted average value of the first guaranteed output coefficient and the second guaranteed output coefficient as a confidence output proportion of the new energy participating in the electric power balance in the same season and the same period.
The process executed by the acquisition module comprises the following steps: determining wind power generation output data and photovoltaic power generation output data;
comparing each data of wind power generation output with synchronous installed capacity to obtain a wind power actual output coefficient set; and comparing each data of the photovoltaic power generation output with the synchronous installed capacity to obtain a photovoltaic actual output coefficient set.
Full-network power load L= { L 1 ，L 2 ，…，L N -a }; wherein L is 1 The 1 st whole network electricity load data; l (L) 2 The 2 nd whole network electricity load data; l (L) N The power load data is used for the Nth whole network; n is the total number of data;
wind power output P W ＝{P W1 ，P W2 ，…，P WN -a }; wherein P is W1 The 1 st wind power output data; p (P) W2 The data is the 2 nd wind power output data; p (P) WN The Nth wind power output data;
photovoltaic output P S ＝{P S1 ，P S2 ，…，P SN -a }; wherein P is S1 Photovoltaic output data 1; p (P) S2 Photovoltaic output data 2; p (P) SN Is the Nth photovoltaic output data;
W n the actual output coefficient of the nth wind power is obtained; g Wn The synchronous wind power installation capacity corresponding to the nth wind power output is obtained;
S n the actual output coefficient of the nth photovoltaic is the actual output coefficient of the nth photovoltaic; g Sn And the synchronous photovoltaic installed capacity corresponding to the nth photovoltaic output is obtained.
The process performed by the first computing module includes:
calculating the ratio R= { R of the daily maximum load to the same-season maximum load 1 ,r 2 ,…，r D -a }; wherein r is d ∈(0,1]D=1, 2 … D, D is the total number of days of raw data;wherein L is dmax For maximum load on day d, L smax The same season maximum load on day d; r is (r) d The ratio of the maximum load on day d to the maximum load in the same season;
screening out sample days meeting a load threshold, screening out daily load peak time and daily load peak time according to the sample days, and respectively connecting the load peak time and the load peak time into a section as a first period of new energy confidence output analysis; and extracting a first sample of the new energy output coefficient from the first period.
After the first sample data are ordered from big to small, setting the preset confidence probability level as beta in the same season and in the same period of time; make i satisfyi is a natural number; the first sample ordering includes:
wind power output coefficient set in first sampleWherein (1)>The 1 st wind power output coefficient in the first sample; />For the 2 nd wind power output coefficient in the first sample；/>The X-th wind power output coefficient in the first sample; x is the number of the first samples after sequencing;
photovoltaic power generation output coefficient set in first sampleWherein (1)>The 1 st photovoltaic power generation output coefficient in the first sample; />The power coefficient of the 2 nd photovoltaic power generation in the first sample;the X-th photovoltaic power generation output coefficient in the first sample;
so the first guarantee output coefficient of wind power under the preset confidence probability beta isThe first guarantee output coefficient of photovoltaic power generation is +.>
Combining local load characteristics of each season to determine the peak period and the late peak period of each season; carrying out per unit treatment on the peak-at-noon period according to the peak-at-noon maximum value in the same season; carrying out per unit treatment on the late peak time period according to the maximum value of the late peak in the same season; carrying out per unit treatment on the data of different years;
and screening a second sample meeting a load threshold from the load data subjected to per unit processing, and screening a second sample of the new energy output coefficient according to the second sample of the load.
After the second sample data are ordered from big to small, setting preset confidence in the same season and the same time periodThe probability level is beta; make j satisfyj is a natural number; the second sample ordering includes: wind power output coefficient set in second sample>Wherein (1)>The 1 st wind power output coefficient in the second sample; />The 2 nd wind power output coefficient in the second sample; />The Y-th wind power output coefficient in the second sample; y is the number of the second samples after sequencing; photovoltaic power generation output coefficient set in second sampleWherein (1)>The 1 st photovoltaic power generation output coefficient in the second sample; />The power coefficient of the 2 nd photovoltaic power generation in the second sample; />The power coefficient of the Y-th photovoltaic power generation in the second sample;
so the second guaranteed output coefficient of wind power under the preset confidence probability beta isThe second guaranteed output coefficient of the photovoltaic power generation is +.>
The confidence output computing system for the participation of the new energy in the electric power balance provided by the embodiment 2 of the invention integrates the safe, reliable, economical and efficient relations, can provide specific guidance for how the new energy participates in the electric power balance, and provides an important basis for the safe and reliable supply of electric power in a large-scale new energy access background.
According to the confidence output calculation system for the new energy participation in the electric power balance, provided by the embodiment 2, aiming at a certain period of a certain season, a weighted average value of the guaranteed output coefficients under a certain load level and a certain confidence probability calculated by two methods is taken as a specific proportion of the new energy participation in the electric power balance of the period, so that the confidence output reliability is improved.
The description of the relevant parts in the confidence output computing system for new energy participation in electric power balance provided in embodiment 2 of the present application may refer to the detailed description of the corresponding parts in the confidence output computing method for new energy participation in electric power balance provided in embodiment 1 of the present application, and will not be repeated here.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements is inherent to. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element. In addition, the parts of the above technical solutions provided in the embodiments of the present application, which are consistent with the implementation principles of the corresponding technical solutions in the prior art, are not described in detail, so that redundant descriptions are avoided.
While the specific embodiments of the present invention have been described above with reference to the drawings, the scope of the present invention is not limited thereto. Other modifications and variations to the present invention will be apparent to those of skill in the art upon review of the foregoing description. It is not necessary here nor is it exhaustive of all embodiments. On the basis of the technical scheme of the invention, various modifications or variations which can be made by the person skilled in the art without the need of creative efforts are still within the protection scope of the invention.
Claims (9)
1. The confidence output calculation method for the participation of the new energy in the electric power balance is characterized by comprising the following steps of:
acquiring historical full-grid power load and new energy output data, and calculating actual output coefficient sets of each new energy according to the new energy output data and the contemporaneous installed capacity; the specific process for acquiring the historical full-grid power load and the new energy output data and calculating the actual output coefficient set of each new energy according to the new energy output data and the contemporaneous installed capacity comprises the following steps:
full-network power load L= { L 1 ，L 2 ，…，L N -a }; wherein L is 1 The 1 st whole network electricity load data; l (L) 2 The 2 nd whole network electricity load data; l (L) N The power load data is used for the Nth whole network; n is the total number of data;
wind power output P W ＝{P W1 ，P W2 ，…，P WN -a }; wherein P is W1 The 1 st wind power output data; p (P) W2 The data is the 2 nd wind power output data; p (P) WN The Nth wind power output data;
photovoltaic output P S ＝{P S1 ，P S2 ，…，P SN -a }; wherein P is S1 Photovoltaic output data 1; p (P) S2 Photovoltaic output data 2; p (P) SN Is the Nth photovoltaic output data;
W n the actual output coefficient of the nth wind power is obtained; g Wn Corresponding to the nth wind power generation forceIs equal to the synchronous wind power installation capacity;
S n the actual output coefficient of the nth photovoltaic is the actual output coefficient of the nth photovoltaic; g Sn The synchronous photovoltaic installed capacity corresponding to the nth photovoltaic output force;
under the same season and the same period, a first sample and a second sample are respectively screened from the actual output coefficient set of the new energy according to a first mode and a second mode, and a first guarantee output coefficient of the first sample under the preset confidence probability is calculated; calculating a second guaranteed output coefficient of the second sample under the preset confidence probability; the first mode is that a load threshold value is firstly determined, a sample day is screened out, and a screening period is determined in the sample day; the second way is to determine a screening period and a load threshold;
and calculating a weighted average value of the first guaranteed output coefficient and the second guaranteed output coefficient as a confidence output proportion of the new energy participating in the electric power balance in the same season and the same period.
2. The method for calculating the confidence output of the new energy participating in the power balance according to claim 1, wherein the method for calculating the actual output coefficient set of each new energy according to the new energy output data and the contemporaneous installed capacity comprises the following steps:
determining wind power generation output data and photovoltaic power generation output data;
comparing each data of wind power generation output with synchronous installed capacity to obtain a wind power actual output coefficient set; and comparing each data of the photovoltaic power generation output with the synchronous installed capacity to obtain a photovoltaic actual output coefficient set.
3. The method for calculating the confidence output of the new energy participating in the power balance according to claim 1, wherein the process of screening the first sample from the actual output coefficient set of the new energy in the same season and in the same time period comprises the following steps:
calculating daily maximum load and same seasonRatio of maximum load r= { R 1 ,r 2 ,…，r D -a }; wherein r is d ∈(0,1]D=1, 2 … D, D is the total number of days of raw data;wherein L is dmax For maximum load on day d, L smax The same season maximum load on day d; r is (r) d The ratio of the maximum load on day d to the maximum load in the same season;
screening out sample days meeting a load threshold, screening out daily load peak time and daily load peak time according to the sample days, and respectively connecting the load peak time and the load peak time into a section as a first period of new energy confidence output analysis; and extracting a first sample of the load and the new energy output coefficient from the first period.
4. The method for calculating the confidence output of a new energy participating in power balance according to claim 3, wherein the process of calculating the first guarantee output coefficient of the first sample under the preset confidence probability comprises the following steps:
after the first sample data are ordered from big to small, setting the preset confidence probability level as beta in the same season and in the same period of time; make i satisfyi is a natural number; the first sample ordering comprises: wind power generation coefficient set in first sample +.>Wherein (1)>The 1 st wind power output coefficient in the first sample; />For the first thingThe 2 nd wind power output coefficient in the method; />The X-th wind power output coefficient in the first sample; x is the number of the first samples after sequencing;
photovoltaic power generation output coefficient set in first sampleWherein,the 1 st photovoltaic power generation output coefficient in the first sample; />The power coefficient of the 2 nd photovoltaic power generation in the first sample; />The X-th photovoltaic power generation output coefficient in the first sample;
so the first guarantee output coefficient of wind power under the preset confidence probability beta isThe first guarantee output coefficient of the photovoltaic power generation is
5. The method for calculating the confidence output of new energy participation in the power balance according to claim 1, wherein the process of screening the second sample according to the time period comprises the steps of:
combining local load characteristics of each season to determine the peak period and the late peak period of each season; carrying out per unit treatment on the peak-to-peak load according to the peak-to-peak maximum value in the same season; carrying out per unit treatment on the load in the late peak period according to the maximum value of the late peak in the same season; carrying out per unit treatment on the data of different years;
and screening a second sample meeting a load threshold from the load data subjected to per unit processing, and screening a second sample of the new energy output coefficient according to the second sample of the load.
6. The method for calculating the confidence output of the new energy participation in the power balance according to claim 5, wherein the process of calculating the second guaranteed output coefficient of the second sample under the preset confidence probability comprises the following steps:
after the second sample data are ordered from big to small, setting the preset confidence probability level as beta in the same season and at the same time period; make j satisfyj is a natural number; the second sample ordering includes: wind power output coefficient set in second sample>Wherein (1)>The 1 st wind power output coefficient in the second sample; />The 2 nd wind power output coefficient in the second sample; />The Y-th wind power output coefficient in the second sample; y is the number of the second samples after sequencing; photovoltaic power generation output coefficient set in second sampleWherein (1)>The 1 st photovoltaic power generation output coefficient in the second sample; />The power coefficient of the 2 nd photovoltaic power generation in the second sample; />The power coefficient of the Y-th photovoltaic power generation in the second sample;
so the second guaranteed output coefficient of wind power under the preset confidence probability beta isThe second guaranteed output coefficient of the photovoltaic power generation is
7. The method for calculating the confidence output of a new energy participation power balance according to claim 6, further comprising calculating a variance sigma of the load maximum value of the load sample points from the first sample after calculating the second guaranteed output coefficient of the second sample at the preset confidence probability 1 And calculating the variance sigma of the load maximum in the second sample from all points of the load sample points 2 The method comprises the steps of carrying out a first treatment on the surface of the Through sigma 1 Sum sigma 2 Calculating the weight of the first guaranteed output coefficient and the weight of the second guaranteed output coefficient; wherein sigma 1 Sum sigma 2 The determining method of (1) comprises the following steps:
under the same season and same period, screening out a first sample of the load, carrying out per unit processing according to the maximum value of the load point by point, respectively processing data of different years, calculating the variance of all points of the sample points of the load deviating from the maximum value of the load in the first sample, and marking as sigma 1 ；
After screening out the second load sample, carrying out per unit processing point by point according to the maximum load value, and after processing the data of different years respectively, calculating that all points of the load sample point deviate from the maximum load value in the second sampleThe variance of (a) is denoted as sigma 2 。
8. The method of claim 7, wherein the step of calculating the weighted average of the first guaranteed output coefficient and the second guaranteed output coefficient as the confidence output ratio of the new energy to the power balance comprises:
under the same season and the same period, the proportion of wind power participating in power balance is as follows:
the proportion of the participation of the photovoltaic power generation in the power balance is as follows:
wherein,said mu 1 The weight of the first guaranteeing output coefficient; mu (mu) 2 And the weight of the second guaranteed output coefficient.
9. The confidence output computing system for the participation of the new energy in the electric power balance is characterized by comprising an acquisition module, a first computing module and a second computing module;
the acquisition module is used for acquiring historical full-grid power load and new energy output data and calculating actual output coefficient sets of each new energy according to the new energy output data and the contemporaneous installed capacity; the specific process for acquiring the historical full-grid power load and the new energy output data and calculating the actual output coefficient set of each new energy according to the new energy output data and the contemporaneous installed capacity comprises the following steps:
full-network power load L= { L 1 ，L 2 ，...，L N -a }; wherein L is 1 The 1 st whole network electricity load data; l (L) 2 The 2 nd whole network electricity load data; l (L) N Number of power loads for Nth full networkAccording to the above; n is the total number of data;
wind power output P W ＝{P W1 ，P W2 ，...，P WN -a }; wherein P is W1 The 1 st wind power output data; p (P) W2 The data is the 2 nd wind power output data; p (P) WN The Nth wind power output data;
photovoltaic output P S ＝{P S1 ，P S2 ，…，P SN -a }; wherein P is S1 Photovoltaic output data 1; p (P) S2 Photovoltaic output data 2; p (P) SN Is the Nth photovoltaic output data;
W n the actual output coefficient of the nth wind power is obtained; g Wn The synchronous wind power installation capacity corresponding to the nth wind power output is obtained;
S n the actual output coefficient of the nth photovoltaic is the actual output coefficient of the nth photovoltaic; g Sn The synchronous photovoltaic installed capacity corresponding to the nth photovoltaic output force;
the first calculation module is used for calculating a first sample and a second sample which are screened from the actual output coefficient set of the new energy source according to a first mode and a second mode respectively under the same season and the same period, and calculating a first guarantee output coefficient of the first sample under the preset confidence probability; calculating a second guaranteed output coefficient of the second sample under the preset confidence probability; the first mode is that a load threshold value is firstly determined, a sample day is screened out, and a screening period is determined in the sample day; the second way is to determine a screening period and a load threshold;
the second calculation module is used for calculating a weighted average value of the first guaranteed output coefficient and the second guaranteed output coefficient as a confidence output proportion of the new energy participating in the electric power balance in the same season and the same period.
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