CN115842354A - Wind power energy storage configuration method for improving wind power prediction correlation coefficient - Google Patents
Wind power energy storage configuration method for improving wind power prediction correlation coefficient Download PDFInfo
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Abstract
The invention discloses a wind power energy storage configuration method for improving a wind power prediction correlation coefficient, which comprises the steps of determining a wind power prediction correlation coefficient calculation mode; adopting a real-time power correction method based on the power change rate of the prediction curve; determining a standard day, and establishing a test data set according to the standard day; constructing an energy storage system model; mutually combining different rated powers and different rated capacities of the stored energy, and inputting the combined configuration into an energy storage system model; and configuring the wind power energy storage according to the adjusting effects of different combinations. According to the method, according to the specific current situation of the wind power prediction correlation coefficient of the actual wind power plant, historical power prediction data and historical actual power data of the wind power plant are used as input, operation promotion effects under different rated powers and rated capacity configurations of stored energy are output through a series of modeling and simulation tests, and scientific guidance is provided for planning and configuring the stored energy of the wind power plant.
Description
Technical Field
The invention relates to the field of new energy electric power, in particular to a wind power energy storage configuration method for improving a wind power prediction correlation coefficient.
Background
The large-scale introduction of wind power can bring huge uncertainty to the whole power system, and the stored energy is used as an important technology capable of carrying out planned time transfer on the energy, so that the uncertainty of the wind power can be effectively leveled, and the stability of the power system is greatly improved.
In the aspect of wind power prediction, due to the incompleteness of meteorological data of domestic wind power plants and the defect of the existing prediction model on complex terrain scenes, the wind power prediction model of the wind power plants is prone to continuous deviation, and the prediction deviation is more obvious in the prediction of medium-long time scales, so that the wind power plants in various regions in China are subjected to penalty due to overlarge wind power prediction deviation, and particularly, the wind power plants in partial regions are subjected to huge penalty due to overlow wind power prediction correlation coefficients. Under the condition that the performance improvement space of the wind power prediction system is limited, energy storage adjustment is a new idea for assessment. The correlation coefficient of wind power prediction can be effectively improved by the stored energy aiming at the correction and adjustment of real-time wind power, the assessment of stations is reduced, and the improvement of the electric energy quality is facilitated.
At present, many energy storage configuration methods and economic evaluation means exist at home and abroad, but the technical route is generally configured based on primary frequency modulation and other auxiliary adjustment economic performance of systems such as thermal power and the like, or based on life cycle economic performance of power grid side energy storage participating in market adjustment, and part of technologies are started aiming at wind power energy storage, but the key point is that the energy storage is configured for stabilizing wind power fluctuation.
Disclosure of Invention
The invention mainly aims to provide a wind power energy storage configuration method for improving a wind power prediction correlation coefficient, which can output operation promotion effects under different rated power and rated capacity configurations of energy storage through a series of modeling and simulation tests according to the specific current situation of wind power prediction correlation coefficient assessment of an actual wind power plant, and provide scientific guidance for planning and configuring energy storage of the wind power plant.
In order to achieve the above object, the wind power storage configuration method for improving the wind power prediction correlation coefficient provided by the present invention includes:
determining a wind power prediction correlation coefficient calculation mode;
adopting a real-time power correction method based on the power change rate of the prediction curve;
determining a standard day, and establishing a test data set according to the detection data of the standard day;
constructing an energy storage system model according to the test data set;
mutually combining different rated powers and different rated capacities of the stored energy, inputting the combined configuration into the energy storage system model, and testing the adjusting effect of each combination in the operation in the selected standard day;
and configuring the wind power energy storage according to the adjusting effects of the different combinations.
In one embodiment, the wind power prediction correlation coefficient is calculated by the following formula:
wherein, X i Predicting the ith value of the power value, Y, for the day ahead i For the ith value of the actual power on the day,
predicting a set of power values for the day aheadCombined arithmetic mean value->
N is the arithmetic mean of the actual power set on the day, and represents the number of the recorded sampling points.
In an embodiment, the step of adopting the real-time power correction method based on the prediction curve power change rate includes specifically controlling the change degree of the real-time output power within the power change rate based on the wind power energy storage according to the power change rate between two adjacent day-ahead prediction power time sampling points of the time point for the actual wind power output power at any time point.
In one embodiment, the determination of the standard day includes the steps of:
the wind power of the standard day is in the wind power interval of the wind power plant;
the wind turbine generators are all started and generate power normally;
no abnormal power jump occurs and no burst limit power condition occurs.
In one embodiment, the configuration range of the energy storage rated power is set according to 1% -20% of the total installed capacity of wind power of the wind power plant.
In one embodiment, the arrangement range of the energy storage rated capacity is set according to 1C of the charge-discharge multiplying power under the condition of a given power.
In one embodiment, the installed capacity of the wind farm is defined as P N The rated power of the energy storage is X i (X 1 ＝1％*P N ,X 2 ＝1.5％*P N ,X 3 ＝2％*P N ,...,X n ＝20％*P N Unit: kW), the energy storage rated capacity is Y j (Y 1 ＝1％*P N ,Y 2 ＝1.5％*P N ,Y 3 ＝2％*P N ,...,Y n ＝20％*P N kW · h), wherein the energy storage rated power and the energy storage rated capacity are closest to an integer multiple of 100 if they are not integer multiples of 100.
In an embodiment, the method further comprises:
selecting a combination configuration, and under the selected combination energy storage configuration, the correlation coefficients of the predicted power and the actual power of the n standard days before the adjustment by using the energy storage system model are respectively R 0,1 ,R 0,2 ,R 0,3 ,...,R 0,n After the energy storage system model adjustment is carried out, the correlation coefficient of the predicted power of n standard days and the corrected actual power is R i,1 ,R i,2 ,R i,3 ,...,R i,n . The correlation coefficient is boosted by the percentage average value R inc_precent The calculation method of (2) is as follows:
according to the technical scheme, the wind power energy storage configuration method for improving the wind power prediction correlation coefficient can be used for outputting the operation promotion effect under different configurations of rated power and rated capacity of energy storage by taking historical power prediction data and historical actual power data of a wind power plant as input according to the specific current situation of the wind power prediction correlation coefficient of the actual wind power plant and through a series of modeling and simulation tests, and providing scientific guidance for planning and configuring the energy storage of the wind power plant.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the structures shown in the drawings without creative efforts.
Fig. 1 is a schematic flow diagram of a wind power storage configuration method for improving a wind power prediction correlation coefficient according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a process for establishing an energy storage system model according to an embodiment of the present invention;
FIG. 3 is a diagram illustrating a statistical model calculated by correlation coefficients according to an embodiment of the present invention;
FIG. 4 is a table of correlation coefficient percent improvement averages after standard day adjustments for different combined configurations of embodiments of the present invention;
FIG. 5 is a table of the profit margins under different configurations of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention provides a wind power energy storage configuration method for improving a wind power prediction correlation coefficient.
As shown in fig. 1, a wind power storage configuration method for improving a wind power prediction correlation coefficient according to an embodiment of the present invention includes:
determining a wind power prediction correlation coefficient calculation mode;
adopting a real-time power correction method based on the power change rate of the prediction curve;
determining a standard day, and establishing a test data set according to the detection data of the standard day;
constructing an energy storage system model according to the test data set;
mutually combining different rated powers and different rated capacities of the stored energy, inputting the combined configuration into the energy storage system model, and testing the adjusting effect of each combination in the operation in the selected standard day;
and configuring the wind power energy storage according to the adjusting effects of the different combinations.
According to the technical scheme, the wind power energy storage configuration method for improving the wind power prediction correlation coefficient can output the operation promotion effect under different rated power and rated capacity configurations of energy storage according to the specific current situation of the wind power prediction correlation coefficient of an actual wind power plant by taking historical power prediction data and historical actual power data of the wind power plant as input through a series of modeling and simulation tests, and provide scientific guidance for planning and configuring the energy storage of the wind power plant.
The wind power prediction correlation coefficient is calculated by adopting the following formula:
wherein, X i Predicting the ith value of the power value, Y, for the day ahead i For the ith value of the actual power on the day,
is the arithmetic mean of the set of predicted power values for the day ahead, is->
N represents the number of recorded samples as the arithmetic mean of the current day's set of actual powers.
Aiming at energy storage regulation aiming at reducing assessment, the wind power plant adopts a real-time power correction method based on a prediction curve power change rate, and for actual wind power output power at any time point, the change degree of the real-time output power is controlled within the power change rate by utilizing the quick output of an energy storage system according to the power change rate (curve slope) between two adjacent day-ahead prediction power time sampling points of the time point. After the energy storage is added, the actual wind power at each time point is corrected, so that the power change rate between each point and the previous point is close to the predicted power change rate of the corresponding prediction interval, namely, the energy storage adjustment is carried out according to a target power change rate value.
In addition, the test data is established for simulation of real wind power plant power generation data. The test data is obtained from historical data of a specific wind power plant in the past year, and from the viewpoint of test effectiveness and accuracy, a reasonable data set comprises annual record data of the wind power plant in at least one year.
Generally, wind conditions of a wind power plant in 12 natural months in one year are different, and prediction results are also different, so that test data need to have a wider coverage range for different conditions, namely historical data under different weather conditions in 12 natural months need to be covered.
The standard day can represent effective data, the standard day reflects a general power change rule and a predicted power change rule in a certain time period, historical data can be selected by a method of selecting the standard day in order to improve the simulation test efficiency, and data characteristics of the standard day are used for representing general conditions in the certain time period.
The selection of the standard day comprises the following steps:
1) The wind power of a standard day is in a common wind power interval of a wind power plant, and different wind power intervals and wind energy resource rich areas are provided for different areas: the wind power interval is more than 200W/m 2 The annual cumulative hours of the wind speed of 3-20 m/s is more than 5000h, and the annual average wind speed is more than 6m/s; the secondary rich area of wind energy resources: the wind power interval is 200-150W/m 2 The annual cumulative hours of the wind speed of 3-20 m/s is 5000-4000 hours, and the annual average wind speed is about 5.5 m/s; the available area of wind energy resources: the wind power interval is 150-100W/m 2 The annual cumulative hours of the wind speed of 3-20 m/s is 4000-2000 h, and the annual average wind speed is about 5 m/s: wind energy resource poor region: the wind power interval is less than 100W/m 2 The annual cumulative hours of the wind speed of 3-20 m/s is less than 2000h, and the annual average wind speed is 4.5m/s.
2) The standard day requires that all wind turbines are started and normally generate power (if the turbines are stopped or the power generation is suspended due to too low wind speed on the day, the full-field power curve cannot reflect the general rule of the full-field power).
3) Abnormal power sudden change (such as power fluctuation amplitude exceeding a normal weather level caused by a special rare weather phenomenon) and sudden power limiting situations (such as a circuit is blocked or damaged, and power reduction operation by a hand is required) can not occur in standard days.
4) The selection of the standard day also needs to meet the following conditions: the correlation coefficient between the predicted curve and the actual curve should include various cases from 0.1 to 0.9.
The energy storage system model evaluates the adjusting effects of different energy storage configurations by simulating a real energy storage system, wind power input of a wind power plant and a real evaluation calculation method. The test model building comprises building of a statistical model, building of an energy storage system model, building of the energy storage system model, building of an economic evaluation module and building of a parameter model. The process of establishing the energy storage system model is shown in fig. 2.
The statistical model is used for calculating and processing statistical data such as correlation coefficient, accuracy and the like of input or output data. The model is mainly a mathematical formula relevant to assessment and calculation, and can be used for evaluating subsequent profit and economy indexes by calculating indexes such as correlation coefficient or accuracy of original predicted power and actual output power of the wind power plant and indexes such as correlation coefficient or accuracy of predicted power and output power after real-time adjustment of the wind power plant, comparing the indexes before and after adjustment, and outputting a lifting effect.
The energy storage system model is used for simulating a real energy storage system, and has the functions of receiving a control instruction in real time to adjust an output power control value, updating an SOC (state of charge) in real time, feeding back a maximum charge-discharge power limit value in real time and the like. The energy storage system model can reflect the energy change condition of the battery system in real time, update the SOC (state of charge) and the charge-discharge capacity state in real time under the condition of maintaining the charge-discharge output of energy storage, and keep the timely execution performance of the control algorithm instruction.
The parameter model refers to a configuration variable model of energy storage, and the method in the invention is to test the combined effect of multiple groups of energy storage rated capacity and configuration, so that the energy storage configuration can be changed in the module to perform simulation test. The specific form of the parameter model in the method is that an enumeration method is used for mutually combining different rated powers and different rated capacities of the stored energy, and the combined configuration is input into the energy storage system model to change the configuration of the stored energy.
According to the policies related to the existing energy storage configuration and the cost of energy storage, the configuration range of the rated energy storage power can be set according to 1% -20% of the total capacity of the wind power installed machine of the wind power plant (the variable interval is 0.5% of the total capacity of the wind power installed machine), and the configuration range of the rated energy storage power can be set according to the charging and discharging multiplying power of 1C under the condition of set power.
Installed capacity of wind power plant is P N Rated power variable is X i (X 1 ＝1％*P N ,X 2 ＝1.5％*P N ,X 3 ＝2％*P N ,...,X n ＝20％*P N kW) unit), rated capacity variable Y j (Y 1 ＝1％*P N ,Y 2 ＝1.5％*P N ,Y 3 ＝2％*P N ,...,Y n ＝20％*P N Unit: kW. H), for each combination (X) i ,Y j ) And (6) carrying out testing. In the above method, for convenience of calculation and implementation, X i And Y j If the value of (b) is not an integer multiple of 100, the value of (b) is closest to the integer multiple of 100.
The adjusting model can output adjusting power in real time according to the input of the wind power plant state, the energy storage state and the wind power prediction curve data in real time. After receiving the input of wind power data, the regulation model calculates a target output power value according to a predicted power value and a real-time power value at a certain time point, reads the state information of energy storage in real time, wherein the state information comprises maximum chargeable power, maximum dischargeable power and SOC (state of charge), outputs the target output power value of energy storage after calculation of the regulation algorithm, and transmits the instruction to the energy storage system model.
The economic evaluation module is used for calculating the full life cycle benefit effect of the energy storage system under the specific configuration combination. The energy storage configuration method provided by the invention is a configuration method for predicting the correlation coefficient based on the wind power lifting, so that the economic evaluation module takes the lifting effect of the correlation coefficient as an evaluation basic item.
Referring to fig. 2, the flow links in fig. 2 are explained.
(1) The method comprises the following steps Representing the process of inputting test data into the energy storage system model. And inputting the screened standard daily test data through the input end of the energy storage system model.
(2) The method comprises the following steps The parametric model configures (P) by combining different power ratings and capacity ratings i ,C j ) Inputting the data into the energy storage system model, and enabling the energy storage system model to be according to (P) i ,C j ) The stored energy configuration controls the stored energy.
(3) The method comprises the following steps The process of acquiring the real-time state information of the energy storage system by the energy storage system model comprises acquiring the real-time output power, SOC (state of charge) and charging and discharging power limit information of the energy storage system. The energy storage system model adjusts the adjusting strategy in real time by acquiring the information in real time.
(4) The method comprises the following steps And the energy storage system model issues an energy storage adjusting instruction. And the energy storage system model outputs the target power of energy storage regulation through calculation of the algorithm model according to the input data, the parameter data and the acquired energy storage real-time state information, and transmits the target power to the energy storage system for execution.
(5) The method comprises the following steps And (3) a process of calculating the correlation coefficient of the input raw data by the statistical model.
(6) The method comprises the following steps And (3) calculating the correlation coefficient of the new wind power plant output data by the statistical model.
(7) The method comprises the following steps And updating the output power of the energy storage system.
(8) The method comprises the following steps And the economic evaluation model calculates the operation promotion effect after the energy storage adjustment, calculates according to the promotion effect by combining the input cost and the expected income of the energy storage system under the specific configuration, comprehensively evaluates the full life cycle profit rate under the energy storage configuration and outputs an energy storage configuration result with the best economic efficiency.
In addition, referring to fig. 3, the method further includes: selecting a combination configuration, and under the selected combination energy storage configuration, the correlation coefficients of the predicted power and the actual power of the n standard days before the adjustment by using the energy storage system model are respectively R 0,1 ,R 0,2 ,R 0,3 ,...,R 0,n After the energy storage system model adjustment is executed, the correlation coefficient of the predicted power of n standard days and the corrected actual power is R i,1 ,R i,2 ,R i,3 ,...,R i,n . The correlation coefficient lifting percentage averagesValue R inc_precent The calculation method of (2) is as follows:
and constructing an energy storage system model according to the steps, testing, analyzing a test result, and selecting an energy storage configuration combination with the optimal profit margin for a specific wind power plant as the energy storage configuration of the wind power plant.
The method has the characteristics of filling the gap of energy storage configuration application of the current wind power plant for solving the problem of wind power prediction deviation, particularly solving the problem of energy storage configuration of the wind power prediction correlation coefficient assessment problem, and providing a set of specific energy storage rated power and rated capacity configuration method. The method has the greatest advantage that the yield of the wind power plant is effectively improved, and in the implemented wind power plant energy storage case, the yield of the wind power plant energy storage optimized configuration performed by the method can be improved by more than 10% compared with the yield of other configurations.
Taking a certain wind farm A in Central China as an example, the specific implementation mode of the method is explained. The wind power plant is located in a high-altitude mountain area, the installed wind power capacity is 99000kW, and a set of energy storage system is expected to be configured for reducing 80% of examination electric quantity of the station, namely corresponding to reduction of examination fine of about 150 ten thousand yuan. The method of the invention is used to select the energy storage capacity and power allocation scheme with the best economical efficiency.
According to the analysis of the wind power plant examination condition in the early stage, the correlation coefficient between the prediction and the actual power of the wind power plant in the area 0-24h before the next day is more than or equal to 0.68, and the wind power plant is marked as unqualified once when the correlation coefficient is less than 0.68. And the grid electricity quantity is checked according to 0.1% of the current month grid electricity quantity of the wind power plant each time. The method for calculating the wind power prediction correlation coefficient r is as follows:
for a certain date, wherein X i Predicting the ith value of the power value, Y, for the day ahead i For the ith value of the actual power on the day,
is the arithmetic mean of the set of predicted power values for the day ahead, is->
N is the arithmetic mean of the actual power set on the day, and represents the number of the recorded sampling points.
Aiming at energy storage regulation aiming at reducing assessment, the wind power plant adopts a real-time power correction method based on a prediction curve power change rate, and for actual wind power output power at any time point, the change degree of the real-time output power is controlled within the power change rate by utilizing the quick output of an energy storage system according to the power change rate (curve slope) between two adjacent day-ahead prediction power time sampling points of the time point. After the energy storage is added, the actual wind power at each time point is corrected, so that the power change rate between each point and the previous point is close to the predicted power change rate of the corresponding prediction interval, namely, the energy storage adjustment is carried out according to a target power change rate value.
The test data is obtained from historical data of the wind power plant in the past year, monthly data of 1 month to 12 months in the last year are obtained, the monthly data comprise a full-field actual power curve and a predicted power curve of the wind power plant in each day of 12 months, and the time resolution is 15min.
In order to reflect the typicality of the data and improve the simulation efficiency, the step takes two dates from each month of the 12-month data as standard days, namely a total of 24 standard days are taken as test objects. The standard day reflects the general power change law and the predicted power change law in the time period. The standard day is selected according to the following rules:
1) The 90% power change interval of the wind power plant is within the interval of 5000kW-50000kW, and the standard day conforms to the characteristics above.
2) In order to further reflect the general rule of the wind power plant, the full-field wind power sets in the typical curve are all started and generate power normally.
3) The power fluctuation range exceeds the normal weather level due to the fact that abnormal power suddenly changes (for example, the power fluctuation range exceeds the normal weather level due to the fact that a special rare weather phenomenon occurs) and sudden limit power situations (a line is blocked or damaged, and power reduction operation is needed by a person) do not occur in the curve.
4) In addition, the correlation coefficient of the day-ahead predicted curve of the standard day with the actual curve includes various cases from 0.1 to 0.8.
According to the selection criteria, 24 standard days (2 per month) are selected as the test data set implemented this time.
And a statistical model of the correlation coefficient is built according to an assessment formula of the region, an energy storage system model is built according to an operation mechanism of a real energy storage system, and a real-time power correction method based on the power change rate of a prediction curve is used for an adjustment model.
For the parameter model, this embodiment uses an enumeration method to combine different rated powers and different rated capacities of the stored energy with each other, test the adjustment effect of each combination operating in the selected standard day, and compare and analyze the results.
Considering the problem of energy storage input cost, enumerating and selecting a variable interval with energy storage rated power from 1000kW to 20000kW, wherein the variable interval is 500kW; the rated capacity is in a variable interval from 1000 kW.h to 20000 kW.h, and the variable interval is 500 kW.h.
Rated power variable X i (X 1 ＝1000,X 2 ＝1500,X 3 ＝2000,...,X 39 = 20000), the rated capacity variable being Y j (Y 1 ＝1000,Y 2 ＝1500,Y 3 ＝2000,...,Y 39 = 20000), for each combination (X) i ,Y j ) And calculating the promotion effect and economic index of operation.
Referring to fig. 4 and 5, fig. 4 is a display of the average percentage of the improvement of the predictive correlation coefficient of the wind farm in the day ahead under the condition of running a real-time adjustment algorithm for different capacity and power combinations of the energy storage system, and a horizontal coordinate and a vertical coordinate of 1000-5000 units are selected as an excerpt display.
Wherein the ordinate in the diagram is the power rating configuration (with P) of the stored energy i Represents, unit: kW), the abscissa is the amount of stored energyConstant volume (using C) j Represents, the unit: kW. H). The values in the graph are specific energy storage power rating and capacity rating combinations (P) i ,C j ) Under the configuration, the algorithm model outputs results aiming at the operation of 24 standard day curves, and the output value is the correlation coefficient promotion percentage average value of 24 standard days.
The correlation coefficient of the day-ahead predicted curve and the actual power curve of 24 standard days before the use of the energy storage regulation is set as R 0,1 ,R 0,2 ,R 0,3 ,...,R 0,24 . In energy storage configuration (P) i ,C j ) On the premise that after the energy storage execution algorithm model is adjusted, the correlation coefficient of the day-ahead predicted curve of 24 standard days and the corrected power curve is R i,1 ,R i,2 ,R i,3 ,...,R i,24 . The data shown in fig. 4 is the average percentage improvement of the day-ahead predicted correlation coefficient calculated according to the formula.
In the aspect of economic evaluation, correlation coefficient failing days in each month are 10-17 days mostly according to wind power prediction correlation coefficient assessment data of the wind power plant over the years, and few individual months are only 4-5 failing days. Therefore, the monthly average failing days are determined as 15 days, and according to preliminary statistics, the correlation coefficient of the days in nearly half of the failing days is around 0.68.
Therefore, in the test experiment of partial historical data, the correlation coefficient and the rate of pass can be improved by about 20% when the correlation coefficient is improved by 1%, namely the assessment of stations corresponding to about 30 ten thousand is reduced. Meanwhile, according to the cost reduction space of the energy storage system in the future, the profit of the energy storage system participating in other adjusting services is estimated and considered, and the profit calculation means adopted by the economic evaluation is that 30 ten thousand annual profits can be obtained when the correlation coefficient is improved by 1%. And because the assessment exemption has an upper limit (the assessment exemption upper limit is 150 ten thousand), the correlation coefficient is assumed to have an upper limit of 5 percent, namely the correlation coefficient is calculated according to 5 percent when being improved by more than 5 percent.
In addition, the total investment in energy storage includes the construction cost of the energy storage device and the maintenance cost over the full life cycle. The construction cost of energy storage comprises the input cost, the capital construction cost, the communication construction cost, the electrical access transformation cost and the like of the energy storage battery equipment. The standard cost of 250 ten thousand RMB is assumed to be calculated according to the implementation cost of the current energy storage market by taking energy storage equipment with the specification of 1000kW/1000 kW.h as a standard, including the investment of cost such as civil engineering construction. Under different configurations of energy storage rated power and rated capacity, the cost is changed, and the change of the power and the capacity needs to involve the amplification of battery capacity, the difference of the type selection of a converter and a transformer and the like. Therefore, the cost for increasing the rated capacity of 1000 kW.h is increased to 180 ten thousand RMB; the cost per 1000kW rated power increase is 10 ten thousand renminbi.
According to the information, the calculation method of the profit margin is combined:
a profit margin display form as shown in fig. 5 can be obtained. According to the information in the graph, the highest value in the numerical values in the table range is the energy storage configuration corresponding to the power and the capacity (2500 kW/5000 kW.h), and the optimal energy storage configuration at this time is 2500kW/5000 kW.h, which can be used as the reference option of the energy storage configuration with the optimal economical efficiency.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention, and all modifications and equivalents of the present invention, which are made by the contents of the present specification and the accompanying drawings, or directly/indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (8)
1. A wind power energy storage configuration method for improving a wind power prediction correlation coefficient is characterized by comprising the following steps:
determining a wind power prediction correlation coefficient calculation mode;
adopting a real-time power correction method based on the power change rate of the prediction curve;
determining a standard day, and establishing a test data set according to the detection data of the standard day;
constructing an energy storage system model according to the test data set;
mutually combining different rated powers and different rated capacities of the stored energy, inputting the combined configuration into the energy storage system model, and testing the adjusting effect of each combination in the operation in the selected standard day;
and configuring the wind power energy storage according to the adjusting effects of the different combinations.
2. The wind power energy storage configuration method for improving the wind power prediction correlation coefficient according to claim 1, wherein the wind power prediction correlation coefficient is calculated by adopting the following formula:
wherein, X i Predicting the ith value of the power value, Y, for the day ahead i For the ith value of the actual power on the day,
is the arithmetic mean of the set of predicted power values the day before,
n represents the number of recorded samples as the arithmetic mean of the current day's set of actual powers.
3. The wind power storage configuration method for improving the wind power prediction correlation coefficient according to claim 1, wherein the step of adopting the real-time power correction method based on the prediction curve power change rate comprises controlling the change degree of the real-time output power within the power change rate based on the wind power storage according to the power change rate between two adjacent day-ahead prediction power time sampling points of the time point for the actual wind power output power at any time point.
4. The wind power energy storage configuration method for improving the wind power prediction correlation coefficient according to claim 1, characterized in that the determination of the standard day comprises the following steps:
the wind power of the standard day is in the wind power interval of the wind power plant;
the wind turbine generators are all started and generate power normally;
no abnormal power jump occurs and no burst limit power condition occurs.
5. The wind power storage configuration method for improving the wind power prediction correlation coefficient according to claim 1, characterized in that the configuration range of the storage rated power is set according to 1% -20% of the total installed wind power capacity of the wind farm.
6. The wind power storage configuration method for improving the wind power prediction correlation coefficient according to claim 5, wherein the configuration range of the storage rated capacity is set according to 1C of charge-discharge multiplying power under a given power condition.
7. The wind power energy storage configuration method for improving the wind power prediction correlation coefficient according to claim 6, characterized in that the installed capacity of the wind power plant is defined as P N The rated power of the stored energy is X i (X 1 ＝1％*P N ,X 2 ＝1.5％*P N ,X 3 ＝2％*P N ,...,X n ＝20％*P N Unit: kW), the energy storage rated capacity is Y j (Y 1 ＝1％*P N ,Y 2 ＝1.5％*P N ,Y 3 ＝2％*P N ,...,Y n ＝20％*P N kW · h), wherein the energy storage rated power and the energy storage rated capacity are closest to an integer multiple of 100 if they are not integer multiples of 100.
8. The wind power storage configuration method for improving the wind power prediction correlation coefficient according to any one of claims 1 to 7, characterized in that the method further comprises:
selecting a combination configuration, and under the selected combination energy storage configuration, the correlation coefficients of the predicted power and the actual power of the n standard days before the adjustment by using the energy storage system model are respectively R 0,1 ,R 0,2 ,R 0,3 ,...,R 0,n After the energy storage system model adjustment is carried out, the correlation coefficient of the predicted power of n standard days and the corrected actual power is R i,1 ,R i,2 ,R i,3 ,...,R i,n . The correlation coefficient is raised by the percentage average R inc_precent The calculation method of (2) is as follows:
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