CN116796540A - Large photovoltaic power station energy storage capacity configuration method considering light rejection rate and prediction precision - Google Patents
Large photovoltaic power station energy storage capacity configuration method considering light rejection rate and prediction precision Download PDFInfo
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Abstract
The invention provides a large-scale photovoltaic power station energy storage capacity configuration method considering the light rejection rate and the prediction precision, which comprises the steps of firstly, deep mining basic data of a photovoltaic power station, analyzing the basic conditions of the annual light rejection phenomenon and the photovoltaic prediction qualification rate of the photovoltaic power station, and further providing a method for calculating the qualification rate and the punishment cost of the photovoltaic prediction in each month; and then, a specific algorithm flow of energy storage capacity configuration is given, a simulation result is obtained through running original data, and an energy storage capacity comprehensive configuration scheme is obtained through weighing comparison of the light rejection rate, the predicted qualification rate and the economy. The invention provides an energy storage configuration technical scheme with practical value for the photovoltaic power station, and realizes automatic analysis of the photovoltaic power station configuration energy storage optimization scheme.
Description
Technical Field
The invention relates to the field of energy storage capacity configuration of new energy power stations, in particular to a large-scale photovoltaic power station energy storage capacity configuration method considering the light rejection rate and the prediction precision.
Background
The photovoltaic power generation has the characteristics of intermittence and volatility, and the power output is unstable. In addition, the power grid dispatching center takes the predicted power of the photovoltaic power station as the planned output, but the predicted value and the actual value still have larger deviation, so that the problem that the actual generated power is not matched with the planned output is caused, the photovoltaic power discarding phenomenon is possibly caused, the problem of safety and reliability caused by insufficient system standby is possibly caused, and in addition, the additional expenditure is increased for the power station facing the examination problem. The energy storage has flexible charge and discharge dual characteristics, plays an indispensable role in stabilizing new energy power fluctuation, promoting green energy high-proportion grid connection, reducing power prediction deviation and the like, and is an important means for solving the grid connection problem of the photovoltaic power station, and promoting energy conservation and emission reduction while ensuring economic benefit. However, the initial investment cost of energy storage is high, and how to perform reasonable capacity allocation has important research significance.
In summary, in recent years, a lot of researches on a light storage capacity configuration method are carried out, but a light storage configuration calculation model considering the light rejection rate and the prediction deviation is few, and the energy storage is configured optimally from the aspect of power stations in terms of technical economy, so that the operation characteristics of the photovoltaic power stations can be improved, the reliability and the environmental protection of the system operation can be indirectly improved, and the theoretical basis can be provided for effectively configuring the energy storage on the photovoltaic power station side.
Disclosure of Invention
In order to solve the problems, the invention provides a large-scale photovoltaic power station energy storage capacity configuration method considering the light rejection rate and the prediction accuracy, and the energy storage capacity comprehensive configuration scheme is obtained by carrying out operation history data simulation and comparing the balance of the light rejection rate, the prediction qualification rate and the economy through the specific algorithm flow of the energy storage capacity configuration provided by the invention, so that the energy storage configuration technical scheme with practical value is provided for the photovoltaic power station, and the feasibility of configuration energy storage at the power station side is further verified, so that the problems in the background technology are solved.
In order to solve the technical problems, the invention adopts the following technical scheme:
according to the Jiangsu assessment rules, the comprehensive configuration scheme for taking assessment indexes and energy storage economy is provided from the power station angle, the automatic extraction analysis of the assessment indexes is performed on annual operation data of a large-scale photovoltaic power station, the case analysis is performed by combining 4000MW photovoltaic power station, the large-scale photovoltaic energy storage configuration calculation model for improving the light rejection rate and the prediction precision is subjected to multi-scene simulation analysis, and the result shows that the comprehensive benefit is optimal when the energy storage capacity is configured to be 11% of the photovoltaic installed capacity, and the punishment cost of the photovoltaic power station is effectively reduced. Therefore, the energy storage optimal scheme configured for the specific photovoltaic power station is combined with the annual operation characteristics of the photovoltaic power station and specific assessment rule analysis, so that the technical economy of the energy storage combined operation of the photovoltaic power station is improved.
The large-scale photovoltaic power station energy storage capacity configuration method considering the light rejection rate and the prediction accuracy comprises the steps of extracting and statistically analyzing characteristic indexes of the photovoltaic power station, so that the light rejection rate and the prediction qualification rate of historical data of the power station are calculated, and the energy storage capacity configuration method considering the assessment indexes and the economy is further established.
A large photovoltaic power station energy storage capacity configuration method considering the light rejection rate and the prediction precision comprises the following steps:
step 1: establishing the characteristics indexes of the light rejection rate and the predicted qualification rate of the photovoltaic power station;
step 2: basic data is input, and parameters are set. Counting photovoltaic prediction qualification rate per month and calculating punishment cost;
step 3: power constraint, so as to screen out a waste light power data set which can be consumed by energy storage;
step 4: energy constraint, avoiding the loss of full charge and discharge to the service life of the battery, and prescribing the charge and discharge capacity of the battery to be 90% of rated capacity;
step 5: outputting simulation results, and outputting technical indexes including energy storage net benefit C, light rejection rate, loss cost and the like;
as a further improvement of the invention, step 1 establishes and calculates characteristic indexes such as the light rejection rate of the photovoltaic power station, and the specific steps are as follows:
step 1-1: through statistics and analysis of a photovoltaic power station sample data set, a formula for measuring and calculating the light rejection power of each sample point is as follows:
p s,i ＝max(0,p a,i -p c,i )#(1)
p s,i optical power p is discarded for the ith sampling point a,i And p c,i The actual power and the predicted power of the ith sampling point are respectively, when p a,i <p c,i At this time, the photovoltaic power generation power will be fully used for responding to the power grid dispatching, if p a,i >p c,i When the photovoltaic power generation power is remained, a certain light rejection phenomenon can be caused.
Step 1-2: establishing and calculating the total monthly light rejection power and the light rejection rate model of the photovoltaic power station;
p s,m for the total light rejection power dataset per month, d (m) represents the total number of days of the mth month, and λ (m) represents the light rejection rate dataset per month.
Step 1-3: establishing a prediction qualification rate model of the photovoltaic power station;
p a,i for the actual power of the ith sample point, p c,i Predicted power for the ith sample point, C ap And (3) counting unqualified points of power prediction by combining with 'Jiangsu province power grid-connected operation management rules' for rated capacity, and calculating punishment cost of each month caused by prediction deviation.
As a further improvement of the invention, step 2 is based on the basic operation data of the photovoltaic power station, and further statistics of the photovoltaic prediction qualification rate of each month and calculation of punishment cost are carried out, and the specific steps are as follows:
step 2-1: inputting data, and calculating the predicted power deviation of each point through the formula (1);
step 2-2: calculating the total sampling point number of each month, determining the specific data range corresponding to the sampling set of each month, and calculating by the formula (5):
s (m) represents the specific data range to which the data of month m corresponds in the annual data set, d (m) represents how many days the month m shares, if and only if m=1,
step 2-3: counting the total number of disqualified points in each month, and obtaining the proportion of disqualified points in each month, wherein the proportion is calculated by the formulas (6) and (7):
N(m)＝{α[S(m)]<90％}#(6)
n (m) is the total number of unqualified points in m months, and alpha [ S (m) ] represents the qualification rate of each point in m months.
The fraction of the number of failed points is the month.
Step 2-4: judging whether the proportion of the disqualified points in the month is more than 2%, if so, calculating the punishment cost of the month caused by the prediction deviation through a formula (8), and if so, setting the punishment cost of the month to be 0.
And (3) making: p a,i -p c,i |＝p p,i Then:
p p,i representing the absolute value of the i-th sample point power prediction bias, C d (m) represents penalty cost due to scheduling bias for m months.
Step 2-5, judging whether the monthly conditions are counted completely, otherwise, returning to the step 1-2;
step 2-6: and outputting the calculation results of the total number of the unqualified points of the month, the unqualified rate, the punishment cost of each month and the like.
As a further improvement of the present invention, step 3 performs power constraint during data operation, so as to screen out a discarded optical power data set that can be consumed by energy storage, and specifically includes the following steps:
step 3-1: calculating the light rejection power of each sample point by the formula (1), and forming a light rejection data set corresponding to the time points one by one;
step 3-2: screening out a waste light power data set which can be consumed by energy storage through a formula (9), if the waste light power at a certain moment is larger than the rated power of the energy storage and cannot be absorbed, defaulting to 0, and updating the power data set which can be consumed by the energy storage;
and the data set is a discarded light power data set which can be consumed by stored energy after screening.
As a further improvement of the present invention, step 4 is to restrict the energy of the stored energy in the charging and discharging process in order to avoid the loss of the battery life caused by the full charge and discharge, and specifically comprises the following steps:
step 4-1: calculating the total electric quantity of the energy storage operation in one day by the formula (10):
E T,j and the total amount of waste light which is consumed by the energy storage battery until the sampling point j is cut off on the T th day.
Step 4-2: in order to avoid the loss of the full charge and discharge to the service life of the battery, the charge and discharge capacity of the battery is specified to be 90% of the rated capacity;
E T,j ＝0.9*E bat #(11)
step 4-3: after the energy storage battery is fully charged in the course of a day, the energy storage battery reaches a saturated state, and the photovoltaic waste light electric quantity is not consumed any more, so that the real-time data set of the consumed waste light power of the battery is updated again:
as a further improvement of the invention, step 5 outputs simulation results and outputs technical indexes including energy storage net gain C, light rejection rate, loss cost and the like
E bat Is of energy storage capacity, p cs Rated power for energy storage, C bat Cost per unit volume, C PCS Is the unit power cost, C pv,g Photovoltaic internet electricity price.
As a further improvement of the present invention, in step 5, the parameter settings are shown in tables 1 and 2.
TABLE 1 Capacity proportioning arrangement
TABLE 2 Primary parameter settings
The invention has the beneficial effects that:
compared with the prior art, the invention has the beneficial effects that: the patent provides a large photovoltaic power station energy storage capacity configuration method considering the light rejection rate and the prediction precision, and the method has the following advantages:
1. according to the Jiangsu assessment rules, the invention provides a comprehensive configuration scheme for taking assessment indexes and energy storage economy from the perspective of a power station;
2. according to the invention, the energy storage optimal configuration scheme of the photovoltaic power station is obtained by discussing the influence of the energy storage configuration scheme on the light rejection rate, the photovoltaic waste electric quantity and the energy storage economy;
3. the method is suitable for analyzing the operation characteristics of the large photovoltaic power station within the annual time scale range, and realizes the automatic analysis of the photovoltaic power station configuration energy storage optimization scheme;
4. according to the invention, through simulation analysis, the comprehensive benefit is optimal when the energy storage capacity is configured to be 11% of the installed capacity of the photovoltaic power station, the highest net benefit of energy storage year can be realized, and the punishment cost of the photovoltaic power station can be effectively reduced.
Drawings
Fig. 1 is a flow chart of the method according to the invention.
Fig. 2 is a graph of the trend of net gain of stored energy.
Fig. 3 is a graph of the change in the light rejection rate and net gain of stored energy.
Fig. 4 is a graph of the trend of annual forecast failure points and penalty costs for photovoltaic plants.
Detailed Description
The invention further provides a large-scale photovoltaic power station energy storage capacity configuration method considering the light rejection rate and the prediction precision by combining the drawings and a specific implementation method.
As shown in fig. 1, a specific implementation flow of the present invention is provided, and a method for configuring energy storage capacity of a large-scale photovoltaic power station in consideration of light rejection rate and prediction accuracy specifically includes the following steps:
step 1: establishing the characteristics indexes of the light rejection rate and the predicted qualification rate of the photovoltaic power station;
step 1-1: through statistics and analysis of a photovoltaic power station sample data set, a formula for measuring and calculating the light rejection power of each sample point is as follows:
p s,i ＝max(0,p a,i -p c,i )#(1)
p s,i optical power p is discarded for the ith sampling point a,i And p c,i The actual power and the predicted power of the ith sampling point are respectively, when p a,i <p c,i At this time, the photovoltaic power generation power will be fully used for responding to the power grid dispatching, if p a,i >p c,i When the photovoltaic power generation power is remained, a certain light rejection phenomenon can be caused.
Step 1-2: establishing and calculating the total monthly light rejection power and the light rejection rate model of the photovoltaic power station;
p s,m for the total light rejection power dataset per month, d (m) represents the total number of days of the mth month, and λ (m) represents the light rejection rate dataset per month.
Step 1-3: establishing a prediction qualification rate model of the photovoltaic power station;
p a,i for the actual power of the ith sample point, p c,i Predicted power for the ith sample point, C ap Is rated capacity and combined with the rule of power grid-connected operation management in Jiangsu provinceAnd counting unqualified points of power prediction, and calculating punishment cost of each month caused by prediction deviation.
Step 2: basic data is input, and parameters are set. Counting photovoltaic prediction qualification rate per month and calculating punishment cost;
step 2-1: inputting data, and calculating the predicted power deviation of each point through the formula (1);
step 2-2: calculating the total sampling point number of each month, determining the specific data range corresponding to the sampling set of each month, and calculating by the formula (5):
s (m) represents the specific data range to which the data of month m corresponds in the annual data set, d (m) represents how many days the month m shares, if and only if m=1,
step 2-3: counting the total number of disqualified points in each month, and obtaining the proportion of disqualified points in each month, wherein the proportion is calculated by the formulas (6) and (7):
N(m)＝{α[S(m)]<90％}#(6)
n (m) is the total number of unqualified points in m months, and alpha [ S (m) ] represents the qualification rate of each point in m months.
The fraction of the number of failed points is the month.
Step 2-4: judging whether the proportion of the disqualified points in the month is more than 2%, if so, calculating the punishment cost of the month caused by the prediction deviation through a formula (8), and if so, setting the punishment cost of the month to be 0.
And (3) making: p a,i -p c,i |＝p p,i Then:
p p,i representing the absolute value of the i-th sample point power prediction bias, C d (m) represents penalty cost due to scheduling bias for m months.
Step 2-5, judging whether the monthly conditions are counted completely, otherwise, returning to the step 1-2;
step 2-6: and outputting the calculation results of the total number of the unqualified points of the month, the unqualified rate, the punishment cost of each month and the like.
Step 3: power constraint, so as to screen out a waste light power data set which can be consumed by energy storage;
step 3-1: calculating the light rejection power of each sample point by the formula (1), and forming a light rejection data set corresponding to the time points one by one;
step 3-2: screening out a waste light power data set which can be consumed by energy storage through a formula (9), if the waste light power at a certain moment is larger than the rated power of the energy storage and cannot be absorbed, defaulting to 0, and updating the power data set which can be consumed by the energy storage;
and the data set is a discarded light power data set which can be consumed by stored energy after screening.
Step 4: energy constraint, avoiding the loss of full charge and discharge to the service life of the battery, and prescribing the charge and discharge capacity of the battery to be 90% of rated capacity;
step 4-1: calculating the total electric quantity of the energy storage operation in one day by the formula (10):
E T,j and the total amount of waste light which is consumed by the energy storage battery until the sampling point j is cut off on the T th day.
Step 4-2: in order to avoid the loss of the full charge and discharge to the service life of the battery, the charge and discharge capacity of the battery is specified to be 90% of the rated capacity;
E T,j ＝0.9*E bat #(11)
step 4-3: after the energy storage battery is fully charged in the course of a day, the energy storage battery reaches a saturated state, and the photovoltaic waste light electric quantity is not consumed any more, so that the real-time data set of the consumed waste light power of the battery is updated again:
step 5: outputting simulation results, and outputting technical indexes including energy storage net benefit C, light rejection rate, loss cost and the like;
E bat is of energy storage capacity, p cs Rated power for energy storage, C bat Cost per unit volume, C PCS Is the unit power cost, C pv,g Photovoltaic internet electricity price.
In step 5, the parameter settings are shown in tables 1 and 2.
TABLE 1 Capacity proportioning arrangement
TABLE 2 Primary parameter settings
To evaluate the feasibility and effectiveness of the present invention, the present invention takes the basic data of sample points 15min,35040 sample at the sample interval of the Belgium photovoltaic power station as an example to study the configuration of the energy storage capacity. The capacity of the photovoltaic power station is 4000MW, and the specification of the file of the Su modified energy source {2021}949 on the energy storage capacity configuration is as follows: the south of Yangtze river is built according to the proportion of 8% of power and above (the duration is two hours, the same applies below); the north region of the Yangtze river is basically configured according to the proportion of 10% of power and above, and the energy storage capacity ratio is set as shown in the table 1 according to the above specification and combined with basic data.
According to the parameter settings in table 1, the calculation example of the invention mainly comprises 6 scenes, the energy storage capacity ratios are 8%,10%,11%,12%,13% and 15%, and the capacities under the ratios are determined by calculating rated powers of different ratios. The energy storage system is considered according to one cycle of a day, namely when the generated energy of the photovoltaic power station is larger than a scheduling plan, residual electricity charges energy storage, and the energy storage system starts discharging at night. The optical storage system is operated in a combined and optimized mode, so that the light rejection rate can be reduced, and the digestion capacity and the prediction accuracy of the photovoltaic power station can be improved. In the above-described method for configuring energy storage capacity based on the assessment index and economy, the basic parameter settings in step 5 are as shown in table 2.
In order to calculate the total number of disqualified points in each month from a huge data set, and thus calculate the penalty cost, the method for calculating the photovoltaic prediction qualification rate in each month and calculating the penalty cost is provided, and the result output after the step 1-2 is completed through calculation is shown in table 3:
TABLE 3 calculation results
From the calculation results in table 3, it is found that the predicted power assessment is not qualified in 8 months in 2021, the penalty cost required to be paid is about 100.62 ten thousand yuan, and the 4 months are qualified without penalty cost, which seriously affects the normal dispatching of the power system and causes additional expenditure of the photovoltaic power station. In summary, the above problems can be solved by configuring energy storage through a photovoltaic power station. Based on the above-mentioned light storage capacity configuration calculation method, the configuration energy storage of the large-scale photovoltaic power station is simulated and calculated, steps 1 to 5 are completed, and the calculation result is shown in a table 4:
TABLE 4 simulation results
In order to more clearly compare the influence rule of the energy storage configuration on each index, the simulation result is analyzed in detail based on the established evaluation index, and the influence of the energy storage capacity on the net cost, the light rejection rate and the predicted qualification rate of the power station is mainly included.
(1) From the aspect of the net benefit of the energy storage, as the capacity of the energy storage configuration increases, the net benefit of the energy storage shows a trend of increasing and decreasing, as shown in fig. 2, because the energy storage is charged when the photovoltaic output is excessive and discharged at night, a certain benefit is obtained, and the net benefit reaches 317 ten thousand when the net benefit is maximum, but when the proportion of the light storage capacity exceeds 11%, the benefit starts to decrease, and when the proportion exceeds 12%, the benefit is even negative.
(2) When the light storage capacity is configured at the lowest ratio of 8%, the annual light rejection rate is lower than 5%, the standard of the clean energy consumption action plan made by the country is met, the light rejection rate is continuously reduced from the highest 4.32% to 1.5% along with the increase of the energy storage configuration capacity, and the photovoltaic utilization rate is greatly improved, as shown in fig. 3, however, with large-scale configuration energy storage, although the light Fu Xiaona rate is improved to a certain extent, when the ratio exceeds 12%, the energy storage income is negative, and therefore, the energy storage economy needs to be comprehensively considered for reducing the light rejection rate.
(3) As the proportion of the optical storage capacity increases, the annual forecast disqualification point is consistent with the change trend of the punishment cost, the punishment cost and the annual forecast disqualification point are continuously reduced as the energy storage configuration capacity is larger, and as shown in fig. 4, when the proportion of the energy storage capacity is 8% and 10%, the punishment cost is unchanged, the annual forecast disqualification point is unchanged, and the configuration capacity is proved to be too small.
When the index analysis result is integrated, the energy storage capacity ratio is 11%, namely 440 MW/660 MWh,2h, the economical efficiency is best, the annual net income is 317 ten thousand when the annual net income is maximum, the predicted power deviation is obviously improved, the light rejection rate is only 2.89%, and the method meets relevant regulations and standards. The method improves the prediction deviation, improves the prediction qualification rate and reduces the punishment cost while improving the economy and the photovoltaic digestion rate. Therefore, when the light storage capacity is 11%, the comprehensive benefit is best.
According to the Jiangsu assessment rules, a comprehensive configuration scheme for taking assessment indexes and energy storage economy is provided from the aspect of a power station, automatic extraction and analysis of the assessment indexes are performed on annual operation data of a large-scale photovoltaic power station, case analysis is performed by combining 4000MW photovoltaic power stations, and a large-scale photovoltaic energy storage configuration calculation model for improving the reject rate and the prediction accuracy is subjected to multi-scene simulation analysis. Therefore, the energy storage optimal scheme configured for the specific photovoltaic power station is combined with the annual operation characteristics of the photovoltaic power station and specific assessment rule analysis, so that the technical economy of the energy storage combined operation of the photovoltaic power station is improved.
The foregoing is only a preferred embodiment of the invention, it being noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the present invention, and such modifications and adaptations are intended to be comprehended within the scope of the invention.
Claims (7)
1. The large-scale photovoltaic power station energy storage capacity configuration method is characterized in that characteristic indexes of the photovoltaic power station are extracted and statistically analyzed, so that the light rejection rate and the prediction qualification rate of historical data of the power station are measured and calculated, and the energy storage capacity configuration method considering assessment indexes and economy is further established.
2. The method for configuring the energy storage capacity of the large photovoltaic power station taking the light rejection rate and the prediction accuracy into consideration as claimed in claim 1, comprising the following steps:
step 1: establishing the characteristics indexes of the light rejection rate and the predicted qualification rate of the photovoltaic power station;
step 2: inputting basic data and setting parameters; counting photovoltaic prediction qualification rate per month and calculating punishment cost;
step 3: power constraint, so as to screen out a waste light power data set which can be consumed by energy storage;
step 4: energy constraint, avoiding the loss of full charge and discharge to the service life of the battery, and prescribing the charge and discharge capacity of the battery to be 90% of rated capacity;
step 5: and outputting a simulation result, and outputting technical indexes including energy storage net benefit C, light rejection rate and loss cost.
3. The method for configuring the energy storage capacity of the large-scale photovoltaic power station taking the light rejection rate and the prediction accuracy into consideration as claimed in claim 2, wherein the step 1 is to build and calculate the characteristic indexes of the light rejection rate and the prediction qualification rate of the photovoltaic power station, and comprises the following specific steps:
step 1-1: through statistics and analysis of a photovoltaic power station sample data set, a formula for measuring and calculating the light rejection power of each sample point is as follows:
p s,i ＝max(0,p a,i -p c,i )#(1)
p s,i optical power p is discarded for the ith sampling point a,i And p c,i The actual power and the predicted power of the ith sampling point are respectively, when p a,i <p c,i At this time, the photovoltaic power generation power will be fully used for responding to the power grid dispatching, if p a,i >p c,i When the photovoltaic power generation power remains, a certain light rejection phenomenon can be caused;
step 1-2: establishing and calculating the total monthly light rejection power and the light rejection rate model of the photovoltaic power station;
p s,m for a total monthly light rejection power dataset, d (m) represents the total number of days of month m, λ (m) represents the light rejection rate dataset for each month;
step 1-3: establishing a prediction qualification rate model of the photovoltaic power station;
p a,i for the actual power of the ith sample point, p c,i Predicted power for the ith sample point, C ap And counting unqualified points of power prediction for rated capacity, and calculating punishment cost of each month caused by prediction deviation.
4. The method for configuring the energy storage capacity of the large-scale photovoltaic power station taking the light rejection rate and the prediction accuracy into consideration as claimed in claim 3, wherein the step 2 is based on the basic operation data of the photovoltaic power station, and further comprises the following specific steps of:
step 2-1: inputting data, and calculating the predicted power deviation of each point through the formula (1);
step 2-2: calculating the total sampling point number of each month, determining the specific data range corresponding to the sampling set of each month, and calculating by the formula (5):
s (m) represents the specific data range to which the data of month m corresponds in the annual data set, d (m) represents how many days the month m shares, if and only if m=1,
step 2-3: counting the total number of disqualified points in each month, and obtaining the proportion of disqualified points in each month, wherein the proportion is calculated by the formulas (6) and (7):
N(m)＝{α[S(m)]<90％}#(6)
n (m) is the total number of unqualified points in the month of m, and alpha [ S (m) ] represents the qualification rate of each point in the month of m;
the proportion of the number of unqualified points in the month;
step 2-4: judging whether the proportion of the disqualified points in each month is more than 2%, if so, calculating punishment cost caused by prediction deviation in the current month through a formula (8), and if so, setting punishment cost in the current month to be 0;
and (3) making: p a,i -p c,i |＝p p,i Then:
p p,i representing the absolute value of the i-th sample point power prediction bias, C d (m) represents penalty cost due to scheduling bias for m months;
step 2-5, judging whether the monthly conditions are counted completely, otherwise, returning to the step 1-2;
step 2-6: and outputting the calculation results of the total number of the unqualified points of the month, the unqualified rate, the punishment cost of each month and the like.
5. The method for configuring the energy storage capacity of the large photovoltaic power station taking the light rejection rate and the prediction accuracy into consideration as set forth in claim 4, wherein the step 3 is to perform power constraint in the data operation process, thereby screening out the light rejection power data set which can be consumed by the stored energy, and specifically comprises the following steps:
step 3-1: calculating the light rejection power of each sample point by the formula (1), and forming a light rejection data set corresponding to the time points one by one;
step 3-2: screening out a waste light power data set which can be consumed by energy storage through a formula (9), if the waste light power at a certain moment is larger than the rated power of the energy storage and cannot be absorbed, defaulting to 0, and updating the power data set which can be consumed by the energy storage;
and the data set is a discarded light power data set which can be consumed by stored energy after screening.
6. The method for configuring the energy storage capacity of the large-scale photovoltaic power station taking the light rejection rate and the prediction accuracy into consideration as claimed in claim 5, wherein in step 4, in order to avoid the loss of the battery life caused by full charge and discharge, the energy of the stored energy in the charge and discharge process is constrained, and the specific steps are as follows:
step 4-1: calculating the total electric quantity of the energy storage operation in one day by the formula (10):
E T,j the total amount of waste light consumed by the energy storage battery from the sampling point of j to the sampling point of j on the T th day;
step 4-2: in order to avoid the loss of the full charge and discharge to the service life of the battery, the charge and discharge capacity of the battery is specified to be 90% of the rated capacity;
E T,j ＝0.9*E bat #(11)
step 4-3: after the energy storage battery is fully charged in the course of a day, the energy storage battery reaches a saturated state, and the photovoltaic waste light electric quantity is not consumed any more, so that the real-time data set of the consumed waste light power of the battery is updated again:
7. the method for configuring the energy storage capacity of the large photovoltaic power station with consideration of the light rejection rate and the prediction accuracy according to claim 6, wherein after all data are circulated in 365 days in one year, step 5 outputs the net energy storage benefit C:
E bat is of energy storage capacity, p cs Rated power for energy storage, C bat Cost per unit volume, C PCS Is the unit power cost, C pv,g Photovoltaic internet electricity price.
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