CN111985671B - Photovoltaic power station generated energy prediction and risk assessment method - Google Patents
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Abstract
The invention discloses a method for predicting the generating capacity of a photovoltaic power station and evaluating risks, which comprises the following steps: step one, predicting the power generation capacity of a photovoltaic power station in the next year; predicting that the power generation amount of the photovoltaic power station in each month in the future year is lower than the expected risk, predicting the paying probability of the power station in each month in the future year according to the historical irradiation of the area where the power station is located, the historical power generation amount in the half year and the predicted power generation amount of the power station in the future year, and reducing the paying risk; and thirdly, monitoring whether the power generation performance of the photovoltaic power station is remarkably attenuated or not, setting early warning measures according to the previous power generation amount and the irradiation value of the power station for the power station performance, evaluating the power station performance once every half month for mechanism early warning, predicting the power generation amount of the power station in the next year, acquiring the related power generation amount in advance, avoiding the loss of economic benefits, making protection measures in advance and improving the actual power generation amount.
Description
Technical Field
The invention belongs to the field of photovoltaic power generation, and particularly relates to a method for predicting the power generation capacity of a photovoltaic power station and evaluating risks.
Background
The photovoltaic power station is a power generation system which is formed by using solar energy and electronic elements made of special materials such as a crystalline silicon plate, an inverter and the like, and is connected with a power grid and transmits power to the power grid. The photovoltaic power station belongs to the green power development energy project with the greatest national encouragement.
It can be divided into an independent power generation system with a storage battery and a grid-connected power generation system without a storage battery. Solar power generation is classified into photo-thermal power generation and photovoltaic power generation. At present, the commercialized solar electric energy is introduced, namely solar photovoltaic power generation.
Photovoltaic power generation products are mainly used in three major areas: firstly, a power supply is provided for a non-electricity occasion; solar electronic products such as various solar chargers, solar street lamps and various solar grassland lamps; thirdly, grid-connected power generation is carried out, which is already popularized and implemented in a large scale in developed countries. By 2009, the grid-connected power generation in china has not started to be fully popularized, however, in 2008, part of electricity used by the beijing olympic conference is provided by solar power generation and wind power generation.
12 and 4 days in 2013, the world maximum scale complementary water and light photovoltaic power station-Longyang gorge complementary water and light 320 megawatt grid-connected photovoltaic power station located in the photovoltaic power generation park of the Cogeneration county of Qinghai province formally starts grid-connected operation, and the problem of poor stability of photovoltaic power generation is solved from the power supply end by utilizing the complementary water and light power generation
The method for predicting the power generation capacity of the photovoltaic power station and evaluating the risk needs to be carried out to achieve the purposes of avoiding the loss of economic benefits, reducing the loss of the power generation capacity of the power station and timely early warning whether the power generation capacity of the power station reaches the standard.
Disclosure of Invention
The invention mainly aims to provide a method for predicting the generating capacity of a photovoltaic power station and evaluating risks, which can effectively solve the problems in the background technology.
In order to achieve the purpose, the invention adopts the technical scheme that:
the method for predicting the power generation capacity of the photovoltaic power station and evaluating the risk comprises the following steps:
step one, predicting the power generation capacity of a photovoltaic power station in the next year;
predicting that the power generation amount of the photovoltaic power station in each month in the future year is lower than the expected risk, predicting the paying probability of the power station in each month in the future year according to the historical irradiation of the area where the power station is located, the historical power generation amount in the half year and the predicted power generation amount of the power station in the future year, and reducing the paying risk;
and step three, monitoring whether the power generation performance of the photovoltaic power station is remarkably attenuated or not, setting early warning measures according to the previous generated energy and irradiation value of the power station for the power station performance, and evaluating the power station performance once every half month to perform mechanism early warning.
Preferably, the power generation prediction of the photovoltaic power plant for the next year comprises the following steps:
s1: calculating a coefficient A, namely actual irradiation (NASA total irradiation from 1 month to 18 months in 18 years and 4 months in 18 years)/theoretical full-time hours (accumulated power generation reference index of the power station from 1 month to 4 months in 18 years), wherein the power generation reference index is the median of the full-time hours of 30 power stations in the same city as the power station;
s2: calculating a coefficient B: establishing a linear regression model without constant terms by taking theoretical full-time hours (power generation amount reference index of a power station every day in 1-3 months at 18 years) as a dependent variable and actual full-time hours of the power station as an independent variable, wherein a fitted independent variable coefficient is a coefficient B; wherein the actual full generation hours is equal to the actual power generation of the power station divided by the installed capacity;
s3: calculating theoretical irradiation in the next year: theoretical irradiation one year in the future, average daily irradiation 365 per year of NASA over the past 20 years;
s4: calculating the full-time number of the power station in the next year: the full-time number of the power station in the next year is theoretical irradiation/coefficient B/coefficient A;
s5: the power generation capacity of the power station in the next year is as follows: the full hours of a power plant in the next year x installed capacity of the power plant.
Preferably, predicting the risk of the generation of electricity by the photovoltaic power plant being below expectations one year and one month in the future comprises the steps of:
step 21: acquiring the irradiation value of the next year of the historical irradiation prediction, acquiring the irradiation of the area of the power station to be predicted in the next year according to the irradiation value provided by the NASA official website, and calculating the formula: the monthly irradiation f (x ═ 1,2,3.. 12) for the next year is equal to the average of the monthly irradiation provided by NASA over the last 20 years;
step 22, observing and calculating historical irradiation distribution, obtaining irradiation data distribution obeying normal distribution according to the irradiation data distribution and the corresponding variation range, and calculating a first coefficient: mean annual irradiation E1Monthly irradiation number and annual irradiation variance V1＝(∑(x-E1) 2)/n, wherein n is the number of months,
the irradiation dose F obeys F to N (E)1,V1)；
Step 23: calculating the data distribution of the generated energy according to the generated energy, wherein the logarithm of the generated energy data obeys normal distribution, and the data distribution of the generated energy obeys normal distribution; calculating the mean value E of the generated energy according to the known generated energy of half a year2Sum variance V2；
Calculating a coefficient two: mean value of power generationE2Generating capacity variance V as the generating capacity/month within half a year2＝(∑(x-E2) ^2)/n, the generated energy P obeys:
P～N(E2,V2)；
step 24: defining a risk assessment value, and solving the risk assessment value through the cross influence of the irradiation and the power generation amount of the area where the power station is located; in steps 22 and 23, it is known that the irradiation obeys normal distribution, the power generation amount obeys normal distribution, and the risk value is defined as irradiation + power generation amount, so that the risk value obeys normal distribution;
step 25: calculating a mean value E of the risk assessment model according to the first and second calculation coefficients obtained in the steps 22 and 233Sum variance V3The calculation formula is as follows:
wherein E1Is the mean value of annual irradiation, V1For annual exposure variance, E2Is the mean value of the generated energy, V2Is the variance of the generated energy;
step 26: computing the arguments of the probabilistic model in step 25: subtracting the actual generated energy P in the current month from the predicted generated energy P in the next year, and taking a logarithm;
step 27: the cumulative probability density found in the probability model found in step 25 is the risk that the power generation amount of the photovoltaic power plant in the future month cannot meet the predicted value after the power generation amount is actually generated in the month, that is, the power generation amount of the photovoltaic power plant in the future year and month is lower than the expected risk.
Preferably, monitoring whether the power generation performance of the photovoltaic power plant has significantly degraded comprises the steps of:
step 31: determining and calculating a power station performance early warning value, calculating a monitoring value according to the actual power generation amount of the power station in half a year and the average irradiation in half a year,
the monitoring value M is P/F30, wherein P is half-year generated energy, and F is NASA monthly irradiation mean value;
the early warning upper limit value is M (1+0.2), the early warning lower limit value is M (1-0.2), and early warning is carried out if the early warning upper limit value is out of the range;
step 32: calculating and predicting the early-half-month early warning value m of the power station, solving the early warning value of the current half-month according to the actual power generation amount of each half-month/NASA actual irradiation in the half-month in the future,
the early warning value m is p/f, wherein p is the actual power generation amount of each half month in the future, and f is the NASA actual irradiation in the half month;
step 33: according to the early warning values M, M obtained in the steps 31 and 32; and if M is larger than M, giving an alarm on the performance of the power station and informing related personnel.
Compared with the prior art, the invention has the following beneficial effects: the method for predicting the power generation capacity of the photovoltaic power station and evaluating the risk comprises the following steps:
the method comprises the following steps that 1, the power generation capacity of a power station in the next year is predicted, the related power generation capacity can be known in advance, the loss of economic benefits is avoided, protective measures are made in advance, and the actual power generation capacity is improved;
predicting that the power generation amount of the photovoltaic power station is lower than an expected risk every month in the future one year, and submitting to predict whether the power generation of the power station reaches the standard or not so as to reduce the loss of the power generation amount of the power station;
and 3, monitoring whether the power generation performance of the photovoltaic power station is remarkably attenuated or not, timely early warning whether the generated energy of the power station reaches the standard or not, and timely notifying related personnel if the generated energy of the power station does not reach the standard, so that the reason of not reaching the standard is checked, and the economic loss is avoided.
Drawings
Fig. 1 is a flow chart of the risk of predicting a power generation below expectations for a photovoltaic power plant one year and one month in the future.
Detailed Description
In order to make the technical means, the creation characteristics, the achievement purposes and the effects of the invention easy to understand, the invention is further described with the specific embodiments.
The method for predicting the power generation capacity of the photovoltaic power station and evaluating the risk comprises the following steps:
step one, predicting the power generation capacity of a photovoltaic power station in the next year;
predicting that the power generation amount of the photovoltaic power station in each month in the future year is lower than the expected risk, predicting the paying probability of the power station in each month in the future year according to the historical irradiation of the area where the power station is located, the historical power generation amount in the half year and the predicted power generation amount of the power station in the future year, and reducing the paying risk;
and step three, monitoring whether the power generation performance of the photovoltaic power station is remarkably attenuated or not, setting early warning measures according to the previous generated energy and irradiation value of the power station for the power station performance, and evaluating the power station performance once every half month to perform mechanism early warning.
Preferably, the power generation prediction of the photovoltaic power plant for the next year comprises the following steps:
s1: calculating a coefficient A, namely actual irradiation (NASA total irradiation from 1 month to 18 months in 18 years and 4 months in 18 years)/theoretical full-time hours (accumulated power generation reference index of the power station from 1 month to 4 months in 18 years), wherein the power generation reference index is the median of the full-time hours of 30 power stations in the same city as the power station;
s2: calculating a coefficient B: establishing a linear regression model without constant terms by taking theoretical full-time hours (power generation amount reference index of a power station every day in 1-3 months at 18 years) as a dependent variable and actual full-time hours of the power station as an independent variable, wherein a fitted independent variable coefficient is a coefficient B; wherein the actual full generation hours is equal to the actual power generation of the power station divided by the installed capacity;
s3: calculating theoretical irradiation in the next year: theoretical irradiation one year in the future, average daily irradiation 365 per year of NASA over the past 20 years;
s4: calculating the full-time number of the power station in the next year: the full-time number of the power station in the next year is theoretical irradiation/coefficient B/coefficient A;
s5: the power generation capacity of the power station in the next year is as follows: the full hours of a power plant in the next year x installed capacity of the power plant.
Preferably, predicting the risk of the generation of electricity by the photovoltaic power plant being below expectations one year and one month in the future comprises the steps of:
step 21: acquiring the irradiation value of the next year of the historical irradiation prediction, acquiring the irradiation of the area of the power station to be predicted in the next year according to the irradiation value provided by the NASA official website, and calculating the formula: the monthly irradiation f (x ═ 1,2,3.. 12) for the next year is equal to the average of the monthly irradiation provided by NASA over the last 20 years;
step 22, observing and calculating historical irradiation distribution, obtaining irradiation data distribution obeying normal distribution according to the irradiation data distribution and the corresponding variation range, and calculating a first coefficient: mean annual irradiation E1Monthly irradiation number and annual irradiation variance V1＝(∑(x-E1) 2)/n, wherein n is the number of months,
the irradiation dose F obeys F to N (E)1,V1)；
Step 23: calculating the data distribution of the generated energy according to the generated energy, wherein the logarithm of the generated energy data obeys normal distribution, and the data distribution of the generated energy obeys normal distribution; calculating the mean value E of the generated energy according to the known generated energy of half a year2Sum variance V2；
Calculating a coefficient two: mean value of electric energy production E2Generating capacity variance V as the generating capacity/month within half a year2＝(∑(x-E2) ^2)/n, the generated energy P obeys:
P～N(E2,V2)；
step 24: defining a risk assessment value, and solving the risk assessment value through the cross influence of the irradiation and the power generation amount of the area where the power station is located; in steps 22 and 23, it is known that the irradiation obeys normal distribution, the power generation amount obeys normal distribution, and the risk value is defined as irradiation + power generation amount, so that the risk value obeys normal distribution;
step 25: calculating a mean value E of the risk assessment model according to the first and second calculation coefficients obtained in the steps 22 and 233Sum variance V3The calculation formula is as follows:
wherein E1Is the mean value of annual irradiation, V1For annual exposure variance, E2Is the mean value of the generated energy, V2Is the variance of the generated energy;
step 26: computing the arguments of the probabilistic model in step 25: subtracting the actual generated energy P in the current month from the predicted generated energy P in the next year, and taking a logarithm;
step 27: the cumulative probability density found in the probability model found in step 25 is the risk that the power generation amount of the photovoltaic power plant in the future month cannot meet the predicted value after the power generation amount is actually generated in the month, that is, the power generation amount of the photovoltaic power plant in the future year and month is lower than the expected risk.
Preferably, monitoring whether the power generation performance of the photovoltaic power plant has significantly degraded comprises the steps of:
step 31: determining and calculating a power station performance early warning value, calculating a monitoring value according to the actual power generation amount of the power station in half a year and the average irradiation in half a year,
the monitoring value M is P/F30, wherein P is half-year generated energy, and F is NASA monthly irradiation mean value;
the early warning upper limit value is M (1+0.2), the early warning lower limit value is M (1-0.2), and early warning is carried out if the early warning upper limit value is out of the range;
step 32: calculating and predicting the early-half-month early warning value m of the power station, solving the early warning value of the current half-month according to the actual power generation amount of each half-month/NASA actual irradiation in the half-month in the future,
the early warning value m is p/f, wherein p is the actual power generation amount of each half month in the future, and f is the NASA actual irradiation in the half month;
step 33: according to the early warning values M, M obtained in the steps 31 and 32; and if M is larger than M, giving an alarm on the performance of the power station and informing related personnel.
The foregoing shows and describes the general principles and broad features of the present invention and advantages thereof. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (3)
1. The method for predicting the power generation capacity of the photovoltaic power station and evaluating the risk is characterized by comprising the following steps of:
step one, predicting the power generation capacity of a photovoltaic power station in the next year;
predicting that the power generation amount of the photovoltaic power station in each month in the future year is lower than the expected risk, predicting the paying probability of the power station in each month in the future year according to the historical irradiation of the area where the power station is located, the historical power generation amount in the half year and the predicted power generation amount of the power station in the future year, and reducing the paying risk;
the method for predicting the risk that the power generation of the photovoltaic power plant is lower than expected in the next year and month comprises the following steps:
step 21: acquiring the irradiation value of the next year of the historical irradiation prediction, acquiring the irradiation of the area of the power station to be predicted in the next year according to the irradiation value provided by the NASA official website, and calculating the formula: the monthly irradiation f (x ═ 1,2,3.. 12) for the next year is equal to the average of the monthly irradiation provided by NASA over the last 20 years;
step 22, observing and calculating historical irradiation distribution, obtaining irradiation data distribution obeying normal distribution according to the irradiation data distribution and the corresponding variation range, and calculating a first coefficient: mean annual irradiation E1Monthly irradiation number and annual irradiation variance V1＝(∑(x-E1) 2)/n, wherein n is the number of months,
the irradiation dose F obeys F to N (E)1,V1)；
Step 23: calculating the data distribution of the generated energy according to the generated energy, wherein the logarithm of the generated energy data obeys normal distribution, and the data distribution of the generated energy obeys normal distribution; calculating the mean value E of the generated energy according to the known generated energy of half a year2Sum variance V2；
Calculating a coefficient two: mean value of electric energy production E2Generated energy/month within half a yearVariance of electric power generation amount V2＝(∑(x-E2) ^2)/n, the generated energy P obeys:
P～N(E2,V2)；
step 24: defining a risk assessment value, and solving the risk assessment value through the cross influence of the irradiation and the power generation amount of the area where the power station is located; in steps 22 and 23, it is known that the irradiation obeys normal distribution, the power generation amount obeys normal distribution, and the risk value is defined as irradiation + power generation amount, so that the risk value obeys normal distribution;
step 25: calculating the mean value E of the probability model according to the first and second calculation coefficients obtained in the steps 22 and 233Sum variance V3The calculation formula is as follows:
wherein E1Is the mean value of annual irradiation, V1For annual exposure variance, E2Is the mean value of the generated energy, V2Is the variance of the generated energy;
step 26: computing the arguments of the probabilistic model in step 25: subtracting the actual generated energy P in the current month from the predicted generated energy P in the next year, and taking a logarithm;
step 27: the cumulative probability density calculated in the probability model calculated in step 25 is the risk that the power generation amount of the photovoltaic power station in the future month cannot meet the predicted value after the power generation amount is actually generated in the current month, that is, the power generation amount of the photovoltaic power station in the future year and month is lower than the expected risk;
and step three, monitoring whether the power generation performance of the photovoltaic power station is remarkably attenuated or not, setting early warning measures according to the previous generated energy and irradiation value of the power station for the power station performance, and evaluating the power station performance once every half month to perform mechanism early warning.
2. The method of claim 1 wherein the photovoltaic power plant power generation prediction and risk assessment comprises the steps of:
s1: calculating a coefficient A, namely actual irradiation/theoretical full-time hours, wherein the actual irradiation is NASA total irradiation in 18 years of 1 month to 18 years of 4 months, the theoretical full-time hours are power generation reference indexes of accumulated power stations in 18 years of 1 month to 4 months, and the power generation reference indexes are median full-time hours of 30 power stations in the same city of the power station;
s2: calculating a coefficient B: taking the theoretical full-time hours as a dependent variable and the actual full-time hours of the power station as an independent variable, establishing a linear regression model without constant terms, wherein the fitted independent variable coefficient is a coefficient B; wherein the actual full generation hours is equal to the actual power generation of the power station divided by the installed capacity; the theoretical full-time hours are the power generation standard index of the power station every 18 years and 1 month to 3 months;
s3: calculating theoretical irradiation in the next year: theoretical irradiation one year in the future, average daily irradiation 365 per year of NASA over the past 20 years;
s4: calculating the full-time number of the power station in the next year: the full-time number of the power station in the next year is theoretical irradiation/coefficient B/coefficient A;
s5: the power generation capacity of the power station in the next year is as follows: the full hours of a power plant in the next year x installed capacity of the power plant.
3. The method of claim 1 wherein monitoring the power generation performance of the photovoltaic power plant for significant degradation comprises the steps of:
step 31: determining and calculating a power station performance early warning value, calculating a monitoring value according to the actual power generation amount of the power station in half a year and the average irradiation in half a year,
the monitoring value M is P/F30, wherein P is half-year generated energy, and F is NASA monthly irradiation mean value;
the early warning upper limit value is M (1+0.2), the early warning lower limit value is M (1-0.2), and early warning is carried out if the early warning upper limit value is out of the range;
step 32: calculating and predicting the early-half-month early warning value m of the power station, solving the early warning value of the current half-month according to the actual power generation amount of each half-month/NASA actual irradiation in the half-month in the future,
the early warning value m is p/f, wherein p is the actual power generation amount of each half month in the future, and f is the NASA actual irradiation in the half month;
step 33: according to the early warning values M, M obtained in the steps 31 and 32; and if M is larger than M, giving an alarm on the performance of the power station and informing related personnel.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2005019870A2 (en) * | 2003-08-20 | 2005-03-03 | New Energy Options, Inc. | Method and system for predicting solar energy production |
| CN106447132A (en) * | 2016-10-26 | 2017-02-22 | 国网浙江省电力公司电力科学研究院 | A medium-and-long term generating capacity prediction method for a regional photovoltaic power station group |
| CN107977898A (en) * | 2017-12-28 | 2018-05-01 | 无锡英臻科技有限公司 | A kind of generated energy Insurance Pricing appraisal procedure of photovoltaic plant |
| CN110766296A (en) * | 2019-10-08 | 2020-02-07 | 杭州雷数科技有限公司 | Ground power station value evaluation model construction system |
Family Cites Families (2)
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|---|---|---|---|---|
| CN106203709A (en) * | 2016-07-13 | 2016-12-07 | 国网江苏省电力公司电力科学研究院 | Based on polyfactorial photovoltaic plant medium-term and long-term generated energy Forecasting Methodology |
| CN108564192B (en) * | 2017-12-29 | 2021-06-08 | 河海大学 | Short-term photovoltaic power prediction method based on meteorological factor weight similarity day |
-
2020
- 2020-04-10 CN CN202010280656.9A patent/CN111985671B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2005019870A2 (en) * | 2003-08-20 | 2005-03-03 | New Energy Options, Inc. | Method and system for predicting solar energy production |
| CN106447132A (en) * | 2016-10-26 | 2017-02-22 | 国网浙江省电力公司电力科学研究院 | A medium-and-long term generating capacity prediction method for a regional photovoltaic power station group |
| CN107977898A (en) * | 2017-12-28 | 2018-05-01 | 无锡英臻科技有限公司 | A kind of generated energy Insurance Pricing appraisal procedure of photovoltaic plant |
| CN110766296A (en) * | 2019-10-08 | 2020-02-07 | 杭州雷数科技有限公司 | Ground power station value evaluation model construction system |
Non-Patent Citations (2)
| Title |
|---|
| "Failure mode prediction and energy forecasting of PV plants to assist dynamic maintenance tasks by ANN based models";Fernando A.Olivencia Polo;《Renewable Energy》;20150930;第227-238页 * |
| "基于辐射量的光伏组件峰值发电小时数评估";范忠瑶;《中国电力教育》;20131129;第232-233页 * |
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