CN110555540A - Method, device and system for evaluating generating capacity of wind power plant - Google Patents

Method, device and system for evaluating generating capacity of wind power plant Download PDF

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Publication number
CN110555540A
CN110555540A CN201810551528.6A CN201810551528A CN110555540A CN 110555540 A CN110555540 A CN 110555540A CN 201810551528 A CN201810551528 A CN 201810551528A CN 110555540 A CN110555540 A CN 110555540A
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wind
uncertainty
value
overall
loss
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CN110555540B (en
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闫瑞
黄启灿
荀玉龙
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Beijing Goldwind Science and Creation Windpower Equipment Co Ltd
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Beijing Goldwind Science and Creation Windpower Equipment Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Abstract

the disclosure provides a method, a device and a system for evaluating the generating capacity of a wind power plant. The method comprises the steps of obtaining a generated energy evaluation parameter file of the wind power plant, a power curve file of the wind generating set and parameters related to a wind resource loss item and an uncertainty item; calculating an overall loss value and an overall uncertainty value of the wind power plant based on the obtained generated energy evaluation parameter file, the power curve file and parameters related to the wind resource loss item and the uncertainty item; establishing a generating capacity probability statistical model of the wind power plant according to the calculated overall loss value and the overall uncertainty value; and calculating the power generation amount of the wind power plant under a specific transcendental probability by using the established power generation amount probability statistical model.

Description

Method, device and system for evaluating generating capacity of wind power plant
Technical Field
The invention relates to the technical field of wind power generation, in particular to a method, a device and a system for evaluating the power generation capacity of a wind power plant.
background
In the wind power industry, the most important link for planning and site selection of a wind power plant is a link for evaluating the generated energy. In the process of evaluating the generated energy, wind measurement data in or around the wind electric field area need to be combined, and a flow field model under a specific geographic environment condition in the wind electric field area needs to be simulated, so that the generated energy is evaluated. The existing power generation evaluation software still has defects in the aspects of terrain simulation, climate (such as factors of thunderstorm, freezing, air density and the like) simulation, flow field simulation and the like. After the power generation amount result is obtained by using the existing power generation amount evaluation software, uncertainty analysis is often required to be performed on the power generation amount result from two aspects of a loss item and an uncertainty item, and the power generation amount capability and uncertainty factors of the wind power plant are quantified to provide an important basis for risk control of the wind power plant.
currently, some power generation amount evaluation software includes a function of uncertainty analysis, but these software only analyze the analysis result of the software ontology, however, there may be interactive analysis of a plurality of software results in actual operation. Due to differences in algorithms, data analysis, and the like between different pieces of software, there may be a calculation bias during the interaction of multiple software results. In addition, the flexibility, the practicability, the operability and the high efficiency of the current power generation capacity evaluation software are still insufficient, for example, the winpro software firstly needs to input measured contour lines and measured wind data when in use, then carries out calculation respectively based on wind statistical results and rsf files output by WT, and then carries out analysis according to various loss values and uncertainty, however, for a mixed-row type wind power plant with complex terrain, the winpro software needs to be used for carrying out repeated operation for many times, and thus a large amount of manpower and time are consumed. Therefore, the existing power generation amount evaluation software is not suitable for requirements of the wind power industry for reducing uncertainty, shortening time period, standardizing operation flow and large-scale operation mode increasingly and obviously, and a software analysis platform which is suitable for service characteristics of the wind power industry and is related to loss items and uncertainty items is urgently needed to be developed in the wind power industry.
Disclosure of Invention
Exemplary embodiments of the present invention provide a method for evaluating power generation of a wind farm, and an apparatus and system thereof, which solve at least the above technical problems and other technical problems not mentioned above and provide the following advantageous effects.
One aspect of the invention provides a method for evaluating the power generation capacity of a wind farm, which may include obtaining a power generation capacity evaluation parameter file of the wind farm, a power curve file of a wind generating set, and parameters related to a wind resource loss term and an uncertainty term; calculating an overall loss value and an overall uncertainty value of the wind power plant based on the obtained generated energy evaluation parameter file, the power curve file and parameters related to the wind resource loss item and the uncertainty item; establishing a generating capacity probability statistical model of the wind power plant according to the calculated overall loss value and the overall uncertainty value; and calculating the power generation amount of the wind power plant under a specific transcendental probability by using the established power generation amount probability statistical model.
Another aspect of the present invention is to provide an apparatus for evaluating power generation of a wind farm, which may include: the data acquisition module is used for acquiring a generated energy evaluation parameter file of the wind power plant, a power curve file of the wind generating set and parameters related to a wind resource loss item and an uncertainty item; the data calculation module is used for calculating an overall loss value and an overall uncertainty value of the wind power plant based on the acquired generated energy evaluation parameter file, the acquired power curve file and parameters related to the wind resource loss item and the uncertainty item; and the model establishing module is used for establishing a power generation probability statistical model of the wind power plant according to the calculated overall loss value and the overall uncertainty value, and calculating the power generation amount of the wind power plant under the specific exceeding probability by using the established power generation probability statistical model.
Another aspect of the invention provides a system for estimating wind farm power generation, the system may include: a browser configured to input and display data; a server configured to: receiving a generated energy evaluation parameter file of a wind power plant, a power curve file of a wind generating set and parameters related to a wind resource loss item and an uncertainty item through a browser; calculating an overall loss value and an overall uncertainty value of the wind farm based on the received power generation amount evaluation parameter file, the power curve file, and parameters related to the wind resource loss term and the uncertainty term; establishing a generating capacity probability statistical model of the wind power plant according to the calculated overall loss value and the overall uncertainty value; and calculating the power generation amount of the wind power plant under a specific transcendental probability by using the established power generation amount probability statistical model.
An aspect of the present invention is to provide a computer-readable storage medium storing a program characterized in that the program may include instructions for executing the above-described method for estimating power generation of a wind farm.
An aspect of the invention is a computer comprising a readable medium having a computer program stored thereon, characterized in that the computer program comprises instructions for carrying out the method for estimating the power generation of a wind farm as described above.
based on the method, the device and the system for evaluating the power generation capacity of the wind power plant, the wind parameters of different machine sites of the wind power plant can be automatically obtained based on the file output by the power generation capacity evaluation software, the sensitivity coefficient of each machine site is analyzed, and then a normal distribution model is established by combining the parameters of the loss item and the uncertainty item to obtain a more accurate power generation capacity evaluation result. In addition, the invention adopts BS architecture design, users can only use browser to realize the processing of the existing data, and do not need to install related software or plug-in, thus the user operation is more convenient.
drawings
the above features and other objects, features and advantages of the present invention will become more apparent from the following detailed description of the present invention when taken in conjunction with the accompanying drawings, in which:
FIG. 1 is a flowchart of a method for assessing wind farm power generation according to an exemplary embodiment of the present disclosure;
FIG. 2 is a detailed flow chart of a method for assessing wind farm power generation according to an exemplary embodiment of the present disclosure;
FIG. 3 is a block diagram of an apparatus for evaluating wind farm power generation according to an exemplary embodiment of the present disclosure;
FIG. 4 is a block diagram of a system for estimating wind farm power generation based on a BS architecture in accordance with an exemplary embodiment of the present disclosure;
Fig. 5a to 5d are diagrams of browser interfaces according to exemplary embodiments of the present disclosure.
Detailed Description
reference will now be made in detail to the exemplary embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the like elements throughout. The embodiments are described below in order to explain the present invention by referring to the figures.
in the present disclosure, terms including ordinal numbers such as "first", "second", etc., may be used to describe various elements, but these elements should not be construed as being limited to only these terms. These terms are only used to distinguish one element from another. For example, a first element could be termed a second element, and vice-versa, without departing from the scope of the present disclosure.
FIG. 1 is a flowchart of a method for assessing wind farm power generation according to an exemplary embodiment of the present disclosure.
Referring to fig. 1, in step S101, a power generation amount evaluation parameter file of a wind farm, a power curve file of a wind turbine generator set, and parameters related to a wind resource loss item and an uncertainty item are acquired. The loss refers to the loss of power generation amount caused by factors such as multiple units or environments in the evaluation process of the wind power plant. For example, parameters related to the wind resource loss term may include parameters related to wake effects, parameters related to availability, parameters related to wind turbine performance, parameters related to electrical, parameters related to the environment, and parameters related to curtailment, among other loss parameters. Wherein the parameters related to wake effects may include losses of wake effects to all wind turbines and losses of future wake effects. Parameters regarding availability may include loss of wind turbine availability, loss of plant balance (substation), loss of grid availability, and other availability losses. Parameters related to fan performance may include losses in power curves, losses in high wind lag, losses in wind flow, and losses in other fan performance. The parameters relating to electrical may include electrical losses as well as equipment losses. The parameters relating to the environment may include loss of performance degradation due to non-freezing, loss of performance degradation due to freezing, loss of downtime due to freezing, lightning, hail, etc., loss of high and low temperatures, loss of wind field opening and other untoward events, loss of tree growth or felling. Parameters regarding curtailment may include loss of sector management, fan zone management, loss of time series, loss of grid curtailment and ramp rate, loss of power purchase protocol curtailment, loss value of noise, loss of flicker, loss of birds, loss of bats. Those skilled in the art will appreciate that the foregoing examples are illustrative only, and that the disclosure is not limited thereto.
uncertainty refers to the uncertainty in the amount of power generated due to the presence of various uncertainty factors in wind resource assessment. For example, parameters related to wind resource uncertainty may include parameters related to wind data, parameters related to a wind model, parameters related to power conversion, and parameters related to losses. The parameters related to the wind data may include, among other things, anemometry/wind data, long term corrections data, annual rate of change data, future climate data, and other wind related data. Parameters for the wind model may include an altitude extrapolation scale factor, a measured altitude extrapolation scale factor, a wind speed level extrapolation scale factor, and other relevant wind model data. Parameters related to power conversion may include data on power curve uncertainty, data on meter uncertainty. The parameter regarding the loss may include parameter data in which the loss is not 0.
In step S102, an overall loss value and an overall uncertainty value of the wind farm are calculated based on the acquired power generation amount evaluation parameter file, the power curve file, and parameters related to the wind resource loss item and the uncertainty item. In the calculation of the overall loss value and the overall uncertainty value of the wind power plant, the overall loss value of the wind power plant is obtained by calculating the mean value of each parameter of all the computer sites related to the loss item, and the overall uncertainty value of the wind power plant is obtained by calculating the mean value of each parameter of all the computer sites related to the uncertainty item. The calculation of the overall loss value and the overall uncertainty value of the wind farm will be described in detail below in fig. 2.
In step S103, a power generation amount probability statistical model of the wind farm is established based on the calculated overall loss value and the overall uncertainty value. In the process of establishing the power generation probability statistical model, firstly, the simulation power generation amount of the wind power plant is obtained from the power generation amount evaluation parameter file, and then the average value and the standard deviation of the normal distribution model can be obtained by combining the integral loss value and the uncertainty value of the wind power plant with the simulation power generation amount, so that the probability distribution density function of the power generation plant is obtained. The probability distribution density function is used to form a cumulative distribution function, i.e., a power generation amount probability statistical model.
further, the present disclosure may also use the loss value and uncertainty value for each machine location point to build a statistical model of the power generation capacity probability for each machine location point. The process of establishing the power generation capacity probability statistical model aiming at each machine position point is the same as the process of establishing the power generation capacity probability statistical model of the wind power plant.
in step S104, the power generation amount of the wind power plant at a specific transcendental probability is calculated by using the established power generation amount probability statistical model. The transcendental probability refers to that if the occurrence of a certain event conforms to a certain distribution, the probability that the event exceeds a certain value is considered as the transcendental probability. In calculating the power generation amount of the wind farm at a specific transcendental probability, an accumulated distribution function regarding the power generation amount is obtained based on the probability distribution density function obtained in step S103, and the power generation amount values at different transcendental probabilities of P50, P75, P84, P95, and the like for 1 year, 5 years, 10 years, and 20 years can be calculated using the accumulated distribution function, respectively. In addition, the accumulated distribution function of each airport point can be established to respectively calculate the power generation values of each airport point under different transcendental probabilities of P50, P75, P84, P95 and the like in 1 year, 5 years, 10 years and 20 years.
FIG. 2 is a detailed flow chart of a method for assessing wind farm power generation according to an exemplary embodiment of the present disclosure.
referring to fig. 2, in step S201, a power generation amount evaluation parameter file of a wind farm and a power curve file of a wind turbine generator set are obtained. For example, the power generation estimate parameter file may be a power generation result table generated using Meteodyn WT software for a particular wind farm. The power curve file of the wind generating set is a power curve table about the wind generating set and can be provided by a manufacturer producing the wind generating set. The above table will not be described in detail here.
In step S202, parameters regarding the wind resource loss term are acquired. The above-mentioned parameter values regarding the wind resource loss term may be obtained through user input according to experience of a wind resource engineer, wind resource data, and the like.
In step S203, parameters regarding the wind resource uncertainty term are acquired. In this step, the parameters of the acquired wind resource uncertainty term are obtained by user input, that is, the parameters of the wind resource uncertainty term acquired in this step are not all of the uncertainty term parameters used in the present invention. For example, in this step, data of uncertainty of the power curve, data of parameter items with loss not being 0, future climate parameters, and the like can be obtained through user input. However, for example, but not limited to, the wind speed vertical extrapolation uncertainty value and the wind speed horizontal extrapolation uncertainty value in the wind model parameters are obtained by the following calculations. Those skilled in the art will appreciate that the foregoing examples are illustrative only, and that the disclosure is not limited thereto.
in step S204, calculating the overall loss value of the wind power plant by using a loss calculation model according to the acquired parameters related to the loss items and the wake flow influence parameters in the power generation amount evaluation parameter file. Specifically, the wake influence parameter in the power generation amount evaluation parameter file is a parameter for each site, where an average value of the wake influence parameter value of each site is required to obtain a wake influence parameter value of the wind farm, and then an overall loss value of the wind farm is calculated according to equation (1) based on the plurality of loss parameters obtained in step S202 and the calculated wind farm wake influence parameter value (i.e., the average value):
Wherein, loss represents the whole loss value, lo _ k represents the parameter values of the obtained different loss terms and the wake flow influence parameter mean value, and k represents the number of the different loss terms.
in step S205, a sensitivity coefficient for each machine location is calculated using a sensitivity coefficient calculation model based on the weibull parameter a value, the K value, the average wind speed for each machine location in the power generation amount evaluation parameter file, and the power curve of the wind turbine generator corresponding to each machine location, respectively. Specifically, first, the raw power generation amount for 1 year of the wind farm is calculated according to equation (2) based on the power curve of the wind turbine generator set corresponding to each machine site at the corresponding air density according to the weibull parameter a value and K value of each machine site in the acquired power generation amount estimation parameter file and the average wind speed:
Wherein aep represents the raw power production, pc (i) represents the corresponding power curve of the unit, a represents the value of the weibull parameter a, K represents the value of the weibull parameter K, i represents the number of wind speed segments, v1(i) represents the starting wind speed of the ith wind speed segment, v2(i) represents the ending wind speed of the ith wind speed segment, and 8760 represents the hours of 1 year.
Secondly, the obtained average wind speed is increased by 5% and decreased by 5%, two wind speeds are obtained, the value of the weibull parameter K is kept unchanged, and a new value of the weibull parameter a is calculated according to equation (3) and equation (4):
a_2＝ws_2/gamma(1+1/k) (3)
a_1＝ws_1/gamma(1+1/k) (4)
Wherein a _2 represents a Weibull parameter A value obtained after the average wind speed is increased by 5%, ws _2 represents a wind speed obtained after the average wind speed is increased by 5%, a _1 represents a Weibull parameter A value obtained after the average wind speed is decreased by 5%, ws _1 represents a wind speed obtained after the average wind speed is decreased by 5%, gamma represents a gamma function, and K represents an original Weibull parameter K value.
After the new weibull parameter a value is calculated, the new weibull parameter a values a _1 and a _2 are respectively substituted into equation (2) to obtain a new power generation amount.
then, the sensitivity coefficient for a certain machine point is calculated according to equation (5):
sen＝((aep_2-aep_1)/aep)/((ws_2-ws_1)/ws) (5)
Wherein sen represents a sensitivity coefficient, aep _2 represents a power generation amount calculated after an average wind speed is increased by 5%, aep _1 represents a power generation amount calculated after an average wind speed is decreased by 5%, ws represents an average wind speed, ws _2 represents a wind speed at which an average wind speed is increased by 5%, and ws _1 represents a wind speed at which an average wind speed is decreased by 5%.
it should be noted that the above-mentioned process of calculating the sensitivity factor is a calculation process performed for each machine location point, that is, the sensitivity factors of different machine location points need to be calculated one by one through the above-mentioned calculation process.
in step S206, a wind speed horizontal extrapolation uncertainty value and a wind speed vertical extrapolation uncertainty value for each machine point are calculated, respectively. Specifically, according to the acquired parameters of the wind model, each machine position point in the generated energy evaluation parameter file and the coordinate information of the wind measuring tower corresponding to each machine position point, the wind speed level extrapolation uncertainty calculation model is combined with the trust coefficient to respectively calculate the wind speed level extrapolation uncertainty value of each machine position point. For example, assuming that a certain site has m anemometers, the wind speed level extrapolation uncertainty value of the ith anemometer for the site is calculated according to equation (6) according to the coordinates of the site in the power generation amount evaluation parameter file, the coordinates of the ith anemometer, and the obtained wind speed level extrapolation proportionality coefficient in the wind model parameters:
ht_i＝((x_0-x_i)^2+(y_0-y_i)^2)^0.5*ht_fac (6)
and hti represents the wind speed level extrapolation uncertainty value of the ith wind measuring tower to the machine site, x _0 represents the abscissa value of the machine site coordinate, x _ i represents the abscissa value of the ith wind measuring tower coordinate, y _0 represents the ordinate value of the machine site coordinate, y _ i represents the ordinate value of the ith wind measuring tower coordinate, and ht _ fac represents the wind speed level extrapolation proportionality coefficient.
then, combining the trust coefficient corresponding to each anemometer tower in the generated energy evaluation parameter table, calculating the wind speed level extrapolation uncertainty value of the computer site according to equation (7):
The method comprises the following steps of calculating a wind speed level extrapolation uncertainty value of an aircraft position point, wherein ht represents a wind speed level extrapolation uncertainty value of the aircraft position point, ht _ i represents a wind speed level extrapolation uncertainty value of the ith anemometer tower to the aircraft position point, coe _ i represents a trust coefficient corresponding to the ith anemometer tower, and m represents the number of the anemometer towers.
in the method, the trust coefficient parameter is used in calculating the wind speed level extrapolation uncertainty value, so that the calculated wind speed level extrapolation uncertainty value is more accurate and reasonable. It should be noted that if the user selects the default trust coefficient, the trust coefficient corresponding to each wind tower is an inverse distance weight calculated from the distance between the wind tower and the aircraft location.
in addition, step S206 further includes calculating a wind speed vertical extrapolation uncertainty value for each machine site using the wind speed vertical extrapolation uncertainty calculation model in combination with the confidence coefficient, respectively, according to the acquired parameters about the wind model, the height information of each machine site and the wind measuring tower corresponding to each machine site in the power generation amount evaluation parameter file, and the hub height information of the wind turbine generator set. For example, assuming that a certain site has m anemometers, based on the altitude and the hub height of the wind turbine of the site in the power generation amount evaluation parameter file, the altitude and the anemometer height of the ith anemometer tower, and the extrapolated proportionality coefficient with respect to the altitude and the extrapolated proportionality coefficient of the measured height in the wind model parameters, the wind speed vertical extrapolated uncertainty value of the ith anemometer tower for the site is calculated according to equation (8):
vt_i＝|(z_0-z_i)|*vt_fac1+|(h_0-h_i)|*vt_fac2 (8)
wherein vt _ i represents a wind speed vertical extrapolation uncertainty value of the ith anemometer tower to the machine location, z _0 represents an altitude of the machine location, z _ i represents the altitude of the ith anemometer tower, h _0 represents a hub height of the machine location, h _ i represents the anemometer height of the ith anemometer tower, vt _ fac1 represents an altitude extrapolation proportionality coefficient, and vt _ fac2 represents a measurement height extrapolation proportionality coefficient.
After the wind speed vertical extrapolation uncertainty values of the m wind measuring towers to the computer site are respectively calculated, the wind speed vertical extrapolation uncertainty values of the computer site are calculated according to equation (9) by combining the trust coefficient corresponding to each wind measuring tower:
Wherein vt represents a wind speed vertical extrapolation uncertainty value of the machine position point, vt _ i represents a wind speed vertical extrapolation uncertainty value of the ith anemometer tower to the machine position point, and coe _ i represents a trust coefficient corresponding to the ith anemometer tower.
in the method, the trust coefficient parameter is used in calculating the wind speed vertical extrapolation uncertainty value, so that the calculated wind speed vertical extrapolation uncertainty value is more accurate and reasonable. It should be noted that if the user selects the default trust coefficient, the trust coefficient corresponding to each wind tower is an inverse distance weight calculated from the distance between the wind tower and the aircraft location.
In step S207, uncertainty values regarding wind speed (i.e., overall first uncertainty value), power curve (i.e., overall second uncertainty value), and loss (i.e., overall third uncertainty value) of the wind farm are calculated, respectively. Specifically, a first uncertainty value of each computer site is calculated separately using a first uncertainty calculation model, and an overall first uncertainty value of the wind farm is calculated using the first uncertainty value of each computer site, according to the acquired parameters on the wind data, the wind speed horizontal and vertical extrapolation uncertainty values calculated in step S206, and the sensitivity coefficient for each computer site. According to the embodiment of the disclosure, when acquiring the anemometry/wind data parameter in the parameters related to the wind data, the wind speed uncertainty value corresponding to the anemometry/wind data can be calculated according to the uncertainty value caused by the anemometer, the uncertainty value caused by the anemometry tower and the uncertainty value caused by the maintenance level of the anemometry tower, which are input by the user. For example, the user may set the uncertainty value caused by the anemometer, the uncertainty value caused by the anemometer tower, and the uncertainty value caused by the maintenance level of the anemometer tower to 0.02, and 0.02, respectively, according to the design experience, the wind speed uncertainty values corresponding to the anemometer/wind data determined by the above three uncertainties may be obtained according to equation (10):
For another example, in acquiring the long-term correction parameter among the parameters regarding the wind data, a wind speed uncertainty value corresponding to the long-term correction parameter may be obtained by a user inputting the number of times of the operation test-related prediction, and in acquiring the annual change rate parameter among the parameters regarding the wind data, a wind speed uncertainty value corresponding to the annual change rate parameter may be obtained by a user inputting the number of years. The above examples are merely illustrative, and the present disclosure is not limited thereto.
after obtaining wind speed uncertainty values corresponding to different wind data parameters, the uncertainty values regarding wind speed for a certain machine site are calculated according to equation (11) using the obtained respective wind speed uncertainty values and the wind speed horizontal extrapolation uncertainty values and the wind speed vertical extrapolation uncertainty values obtained in step S206, in combination with the sensitivity coefficient calculated in step S205:
Wherein uncertainties 1 represents an uncertainty value regarding the power generation amount, i.e., a first uncertainty value, un _ k represents a wind speed uncertainty value corresponding to the kth parameter item in the parameters regarding the wind data wind model, and sen represents a sensitivity coefficient.
after the first uncertainty value of each machine position point is calculated, summing the first uncertainty values of all the machine position points, and dividing by the number of the machine position points to obtain the integral first uncertainty value of the wind power plant.
after the overall first uncertainty value is calculated, a second uncertainty value is calculated using a second uncertainty calculation model based on the acquired parameters regarding power conversion. For example, when obtaining the power curve uncertainty value in the parameter related to the power conversion, the value may be directly input as the power curve uncertainty value by the user according to the design experience. The uncertainty value for a power curve for a certain locus can be calculated according to equation (12):
where uncertainties 2 denotes an uncertainty value regarding the power curve, i.e., a second uncertainty value, and un _ k denotes an uncertainty value corresponding to the kth parameter item in the parameters regarding the power curve.
After the second uncertainty value of each machine position point is calculated, the second uncertainty values of all the machine position points are summed, and then the sum is divided by the number of the machine position points, so that the whole second uncertainty value of the wind power plant is obtained.
After calculating the overall second uncertainty value, a third uncertainty value is calculated using a third uncertainty calculation model based on the obtained parameters related to the loss term. For example, in acquiring the parameters related to the loss term, the user inputs the loss values of different loss terms and the standard deviation values corresponding to the loss values according to the design experience and with reference to the power generation amount loss standard. After obtaining the parameters related to the loss term, the uncertainty value about the loss for a certain machine location can be calculated according to equation (13):
Where uncertainties 3 denotes an uncertainty value regarding loss, i.e., a third uncertainty value, lo _ denotes a k-th loss value among the parameters regarding loss, and sd _ k denotes a standard deviation corresponding to the k-th loss value.
After the third uncertainty value of each machine position point is calculated, the third uncertainty values of all the machine position points are summed, and then the sum is divided by the number of the machine position points, so that the integral third uncertainty value of the wind power plant is obtained.
In step S208, an overall uncertainty value of the wind farm is calculated based on the calculated overall first uncertainty value, the overall second uncertainty value, and the overall third uncertainty value. Specifically, from the overall first uncertainty value, the overall second uncertainty value, and the overall third uncertainty value calculated in step S207, the overall uncertainty value of the wind farm is calculated using equation (14):
uncertainty＝(uncer1^2+uncer2^2+uncer3^2)^0.5 (14)
The uncertainties represent overall uncertainty values of the wind power plants, the uncer1 represents an overall first uncertainty value of the wind power plants, the uncer2 represents an overall second uncertainty value of the wind power plants, and the uncer3 represents an overall third uncertainty value of the wind power plants.
In step S209, a normal distribution model is established based on the calculated overall loss value and overall uncertainty value of the wind farm to obtain a probability distribution density function. Specifically, first, the simulated power generation amount of the wind farm in the power generation amount evaluation parameter file is obtained, and a normal distribution model is established in combination with the overall loss value calculated in step S204 and the overall uncertainty value calculated in step S208. Wherein the mean of the normal distribution model is calculated according to equation (15):
μ＝aep_real*(1-loss) (15)
Where μ denotes a mean value of the normal distribution model, aep _ real denotes a simulation power generation amount, and loss denotes an overall loss value.
The standard deviation of the normal distribution model can be calculated according to equation (16):
σ＝aepreal*(1-loss)*uncertainty (16)
where σ represents the standard deviation of the normal distribution model, aeprealRepresenting the simulation power generation amount, loss representing the integral loss value, and uncertaintiy representing the wholeAn uncertainty value.
after obtaining the mean and standard deviation of the normal distribution model, a probability distribution density function as shown in the following equation (17) can be obtained:
Note that the probability distribution density function in step S209 is a power generation amount probability distribution density function of the wind farm. Further, the calculation process of step S209 may be used to obtain the power generation amount probability distribution density function for each site, that is, the probability distribution density function for each site may be obtained according to the calculation process of step S209, based on the parameter of each site in the power generation amount estimation parameter file, the parameter related to the loss term and the uncertainty term for each site, and the block power curve of the site.
in step S210, a cumulative distribution function is obtained from the probability distribution density function obtained in step S209, and the power generation amount at different transcendental probabilities of the wind farm is calculated using the cumulative distribution function.
Specifically, after obtaining the probability distribution density function as shown in equation (17), the cumulative distribution function as shown in equation (18) below can be directly obtained:
using the cumulative distribution function as shown in equation (18), the power generation at different transcendental probabilities can be obtained. For example, when the overrun probability is P50, the following equation (19) can be obtained:
F(x；μ，σ）＝1-0.5 (19)
Wherein x represents the amount of power generation.
When the overrun probability is P75, the following equation (20) can be obtained:
F（x；μ，σ）＝1-0.75 (20)
Wherein x represents the amount of power generation.
when the overrun probability is P84, the following equation (21) can be obtained:
F(x；μ，σ)＝1-0.84 (21)
Wherein x represents the amount of power generation.
the above examples are merely illustrative, and the present disclosure is not limited thereto.
it should be noted that, according to the embodiment of the present disclosure, when the annual change rate parameter is input, only 1 year may be input. The power generation amount under the different calculated transcendental probabilities is the power generation amount of one year. When the power generation amount of n (n is more than or equal to 2) years is calculated, the annual change rate can be modified (namely, the original annual change rate is divided by the original annual change rate)) And recalculating to obtain new uncertainty so as to obtain new normal distribution model parameters, and calculating the power generation amount under different transcendental probabilities for n years in a manner similar to the manner of calculating the power generation amount under different transcendental probabilities for 1 year.
According to the present disclosure, the power generation at a specific transcendental probability for each machine site may be calculated, as well as the power generation at a specific transcendental probability for a wind farm.
FIG. 3 is a block diagram of an apparatus for evaluating wind farm power generation according to an exemplary embodiment of the present disclosure.
Referring to fig. 3, an apparatus 300 for evaluating power generation of a wind farm may include a data acquisition module 301, a data calculation module 302, and a model building module 303. The data obtaining module 301 may be configured to obtain a power generation amount evaluation parameter file of the wind farm, a power curve file of the wind turbine generator system, and parameters related to a wind resource loss item and an uncertainty item. The data calculation module 302 is used for calculating an overall loss value and an overall uncertainty value of the wind power plant based on the acquired power generation amount evaluation parameter file, the power curve file and parameters related to the wind resource loss item and the uncertainty item. The model building module 303 is configured to build a power generation probability statistical model of the wind farm according to the calculated overall loss value and the overall uncertainty value, and calculate the power generation amount of the wind farm at a specific transcendental probability by using the built power generation probability statistical model.
According to an embodiment of the present disclosure, the acquired parameters related to the loss term may include parameters related to wake effects, parameters related to effectiveness, parameters related to wind turbine performance, parameters related to electrical, parameters related to environment, parameters related to curtailment, and the like, and the acquired parameters related to the uncertainty term may include parameters related to wind data, parameters related to a wind model, parameters related to power conversion, parameters related to loss, and the like.
Specifically, the data calculation module 302 may calculate the overall loss value of the wind farm using the loss calculation model according to the obtained parameters related to the loss item and the wake influence parameters in the power generation amount evaluation parameter file. It should be noted that in calculating the overall loss value of the wind farm, it is necessary to sum the wake influence parameters of each machine location, then divide by the number of machine locations to obtain an average value, and then use equation (1) to obtain the overall loss value of the wind farm in combination with the parameters related to the loss term input by the user.
The data calculation module 302 may calculate a wind speed level extrapolation uncertainty value for each machine site separately using a wind speed level extrapolation uncertainty calculation model in combination with a trust coefficient according to the acquired parameters about the wind model, each machine site in the generated power generation evaluation parameter file, and the coordinate information of the wind tower corresponding to each machine site. And respectively calculating the wind speed vertical extrapolation uncertainty value of each machine position by using a wind speed vertical extrapolation uncertainty calculation model and combining a trust coefficient according to the acquired parameters of the wind model, the height information of each machine position point in the generated energy evaluation parameter file, the height information of the wind measuring tower corresponding to each machine position point and the hub height information of the wind generating set. In addition, data calculation module 302 may sum the wind speed vertical extrapolation uncertainty values for each machine site and then divide by the number of machine sites in the wind farm to obtain a wind speed vertical extrapolation uncertainty value for the wind farm. The wind speed level extrapolation uncertainty value of the wind farm can be obtained by using the same method.
the data calculation module 302 may calculate the sensitivity coefficient of each machine location by using a sensitivity coefficient calculation model according to the weibull parameter a value, the K value, the average wind speed of each machine location in the generated energy evaluation parameter file and the power curve of the wind turbine generator set corresponding to each machine location. The calculation method for obtaining the sensitivity coefficient is the same as that in step S205, and is not described herein again.
The data calculation module 302 may calculate a first uncertainty value for each computer site separately using a first uncertainty calculation model and an overall first uncertainty value for the wind farm using the first uncertainty value for each computer site based on the obtained parameters for the wind data, the calculated wind speed horizontal and vertical extrapolated uncertainty values, and the sensitivity coefficient for each computer site. The data calculation module 302 may further calculate a second uncertainty value for each of the machine sites separately using a second uncertainty calculation model based on the obtained parameters regarding power conversion, and calculate an overall second uncertainty value for the wind farm using the second uncertainty value for each of the machine sites. And respectively calculating a third uncertainty value aiming at each machine position point by using a third uncertainty calculation model according to the acquired parameters related to the loss items, and calculating the overall third uncertainty value of the wind power plant by using the third uncertainty value of each machine position point. The method for calculating the overall first uncertainty value, the overall second uncertainty value, and the overall third uncertainty value of the wind farm is the same as step S207, and is not described herein again.
After obtaining the overall first uncertainty value, the overall second uncertainty value, and the overall third uncertainty value, the data calculation module 302 may calculate the overall uncertainty value of the wind farm according to the calculated overall first uncertainty value, the calculated overall second uncertainty value, and the calculated overall third uncertainty value.
The model establishing module 303 may establish a normal distribution model to obtain a probability distribution density function of the wind farm based on the calculated overall loss value and the overall uncertainty value, and calculate the power generation amount of the wind farm at different transcendental probabilities using a cumulative distribution function formed by the probability distribution density function. In addition, the model building module 303 may also build a normal distribution model for each of the machine sites based on the loss and uncertainty of each of the machine sites to obtain a probability distribution density function for each of the machine sites. That is, the probability distribution density function for each machine site needs to be based on the overall loss and the overall uncertainty of each machine site. For establishing a probability distribution density function of the wind power plant, the integral loss and the integral uncertainty of each machine point need to be averaged to obtain the integral loss and the integral uncertainty of the wind power plant.
Although part of the existing power generation amount evaluation software realizes the function of uncertainty analysis based on the power generation amount evaluation result of the wind power plant to a certain extent, the part of the software is designed based on a client/server (C/S) architecture and cannot be integrated with other software based on a browser/server (B/S) architecture to form complete business process operation. Meanwhile, as packaged desktop software packages or toolkits, since the software needs a user to install a related software package running program on a computer when in use and the maintenance and running costs of a client are high, the software is not suitable for being applied to current large-scale intensive business process operations.
FIG. 4 is a block diagram of a system for estimating wind farm power generation based on a BS architecture according to an exemplary embodiment of the present disclosure.
Referring to fig. 4, the system 400 shown in fig. 4 may include a browser 401 and a server 402. Therein, the browser 401 is configured to input data and display data. According to an example embodiment of the present disclosure, the browser 401 may input data and display data, respectively, in a paginated form. For example, referring to fig. 5a, the user may click an "import" button on a file page of the browser 401 to import the power generation amount evaluation parameter file and the crew power curve file, respectively, to transmit to the server. When the electric power generation amount evaluation parameter file is imported, the imported file can be displayed in a "display imported file" frame for a user to view. Meanwhile, the server 402, after receiving the power generation amount evaluation parameter file, may parse and read parameters such as the number of fans, the annual power generation amount, the average wind speed, etc. in the file, and then transmit the read parameters to a file page to be displayed at the position shown in fig. 5 a.
on the loss page of the browser 401, as shown in fig. 5b, the user can input parameter values of different loss items with reference to the power generation amount loss criterion and the analysis judgment according to the specific item situation. For example, the user may input parameters relating to wake effects, parameters relating to availability, parameters relating to wind turbine performance, parameters relating to electrical, parameters relating to the environment, and parameters relating to curtailment, respectively. Wherein the parameters related to wake effects may include losses of wake effects to all wind turbines and losses of future wake effects. Parameters regarding availability may include loss of wind turbine availability, loss of plant balance (substation), loss of grid availability, and other availability losses. Parameters related to fan performance may include losses in power curves, losses in high wind lag, losses in wind flow, and losses in other fan performance. The parameters relating to electrical may include electrical losses as well as equipment losses. The parameters relating to the environment may include loss of performance degradation due to non-freezing, loss of performance degradation due to freezing, loss of downtime due to freezing, lightning, hail, etc., loss of high and low temperatures, loss of wind field opening and other untoward events, loss of tree growth or felling. Parameters regarding curtailment may include loss of sector management, fan zone management, loss of time series, loss of grid curtailment and ramp rate, loss of power purchase protocol curtailment, loss value of noise, loss of flicker, loss of birds, loss of bats. The above examples are merely exemplary, and those skilled in the art may add other loss term parameters or reduce some of the above loss terms according to design requirements. The server 402 performs subsequent calculations via data input by the browser 401.
On the uncertainty page of browser 402, as shown in fig. 5c, the user can enter parameter values for different loss terms based on known power generation data and design experience. The parameters related to wind resource uncertainty may include parameters related to wind data, parameters related to a wind model, parameters related to power conversion, and parameters related to losses. The parameters related to the wind data may include, among other things, anemometry/wind data, long term corrections data, annual rate of change data, future climate data, and other wind related data. Parameters for the wind model may include an altitude extrapolation scale factor, a measured altitude extrapolation scale factor, a wind speed level extrapolation scale factor, and other relevant wind model data. Parameters related to power conversion may include data on power curve uncertainty, data on meter uncertainty. The parameter regarding the loss may include parameter data in which the loss is not 0. It should be noted that some of the parameters regarding the uncertainty term are directly input by the user, for example, the anemometry/wind data, the long-term correction, the annual rate of change, and the like. Some of the parameters are calculated by the server, such as the wind speed vertical extrapolation uncertainty and the wind speed horizontal extrapolation uncertainty. The foregoing is merely exemplary and the disclosure is not limited thereto.
After receiving the power generation amount evaluation parameter file, the unit power curve file, and the parameters related to the loss item and the uncertainty item through the browser, the server 402 may calculate the overall loss value of the wind farm by using a loss calculation model according to the acquired parameters related to the loss item and the wake influence parameters in the power generation amount evaluation parameter file, and may also calculate the loss value of each computer site.
the server 402 may calculate the sensitivity coefficient for each machine location separately using the sensitivity coefficient calculation model according to the weibull parameter a value, K value, average wind speed for each machine location in the power generation amount evaluation parameter file, and the power curve of the wind turbine generator set corresponding to each machine location.
The server 402 may calculate a wind speed horizontal extrapolation uncertainty value for each machine site using a wind speed horizontal extrapolation uncertainty calculation model in combination with a trust coefficient based on the acquired parameters about the wind model, each machine site in the power generation amount evaluation parameter file, and coordinate information of the wind tower corresponding to each machine site, and calculate a wind speed vertical extrapolation uncertainty value for each machine site using a wind speed vertical extrapolation uncertainty calculation model in combination with a trust coefficient based on the acquired parameters about the wind model, height information of each machine site in the power generation amount evaluation parameter file and the wind tower corresponding to each machine site, and hub height information of the wind turbine generator set.
Server 402 may use equation (11) to separately calculate a first uncertainty value for each machine site based on the obtained parameters for the wind data, the calculated wind speed horizontal and vertical extrapolated uncertainty values, and the sensitivity coefficient for each machine site. After the first uncertainty value of each machine position point is calculated, summing the first uncertainty values of all the machine position points, and dividing by the number of the machine position points to obtain the integral first uncertainty value of the wind power plant. The server 402 may calculate the second uncertainty value using equation (12) according to the acquired parameter regarding the power conversion. After the second uncertainty value of each machine position point is calculated, the server 402 sums the second uncertainty values of all the machine position points, and then divides the sum by the number of the machine position points, thereby obtaining the overall second uncertainty value of the wind farm. The server 402 may calculate the third uncertainty value using equation (13) based on the obtained parameter related to the loss term. After the third uncertainty value of each machine location point is calculated, the server 402 may sum the third uncertainty values of all the machine location points, and then divide by the number of the machine location points, thereby obtaining an overall third uncertainty value of the wind farm.
the server 402 may calculate the overall uncertainty value of the wind farm using equation (14) from the calculated overall first uncertainty value, the overall second uncertainty value, and the overall third uncertainty value.
The server 402 may obtain the simulated power generation amount of the wind farm in the power generation amount evaluation parameter file, and establish a normal distribution model by combining the calculated overall loss value and the calculated overall uncertainty value. Wherein the mean μ of the normal distribution model is calculated according to equation (15) and the standard deviation σ of the normal distribution model is calculated according to equation (16), thereby obtaining a probability distribution density function.
The server 402 may obtain a cumulative distribution function according to the obtained probability distribution density function, and calculate the power generation amount of the wind farm at different transcendental probabilities by using the cumulative distribution function.
The calculation process performed by the server 402 is described in detail in fig. 2, and is not described herein again.
The server 402 may transmit the calculated data to the browser 401 so that the browser 401 visually displays the data calculated in the server. Referring to fig. 5d, on the display result page of the browser 401, the power generation amounts at different override probabilities about the wind farm transmitted by the server 402, for example, the power generation amount values of the wind farm at different override probabilities of P50, P75, P84, P95, etc. within 1 year, 5 years, 10 years, 20 years, may be displayed. And on the display result page, the calculated overall loss value and overall uncertainty value of the wind power plant, and the sub-value of each loss item and the sub-value of each uncertainty item can be displayed. For example, each loss term can be shown in relation to the overall loss in a pie chart effect based on the ratio of the value of each loss term to the overall loss value. The relation between each uncertainty item and the overall uncertainty can be displayed by a pie chart effect according to the proportion of the value of each uncertainty item to the overall uncertainty value. The pie chart for the wear term and the pie chart for the uncertainty may be displayed in a "show wear results" box and a "show uncertainty results" box, respectively. The above examples are merely illustrative, and the present disclosure is not limited thereto.
Further, the server 402 may generate a report of the calculation result from the calculated various data. The server 402 may analyze the xlsx-formatted power curve file and the power generation amount evaluation parameter file uploaded by the user, and read parameters such as the number of fans, annual power generation amount, unit power generation amount, average wind speed, and sensitivity. Through the parameters and the parameters contained in the request sent by the browser, a calculation interface written by python language is called by the backend service of java to calculate, so that main results (namely, the power generation amount (GWh/y), the capacity coefficient (%), the full load hours (h/y) and the like) and a report can be generated. A report generation function button may be provided on the display result page, and when the user clicks the report generation function button, the server 402 may download the generated power generation amount report on the loss and uncertainty to a user-specified location for the user to read and analyze the calculated result.
The method for evaluating wind farm power generation according to an example embodiment of the present disclosure may be implemented as computer readable instructions on a computer readable recording medium or may be transmitted over a transmission medium. The computer readable recording medium is any data storage device that can store data which can be thereafter read by a computer system. Examples of the computer readable recording medium include, but are not limited to, read-only memory (ROM), random-access memory (RAM), compact discs (CD-ROMs), Digital Versatile Discs (DVDs), magnetic tapes, floppy disks, and optical data storage devices. The transmission medium may include a carrier wave transmitted over a network or various types of communication channels. The computer readable recording medium can also be distributed over network coupled computer systems so that the computer readable instructions are stored and executed in a distributed fashion.
based on the method and the system for evaluating the power generation capacity of the wind power plant, processing, analysis and display of various items of loss and uncertainty of the wind power plant based on the BS framework are achieved, a user client can achieve result display of the loss and uncertainty analysis of the wind power plant through a browser, software does not need to be installed, the use requirement of multiple users is met, and the installation cost of the software is simplified. In addition, a user can directly upload data to the server through the browser, data processing can be carried out by inputting fixed parameters, data do not need to be converted and set for many times, the operation flow of software is simplified, and software deviation and redundancy of multi-software repeated function operation in the existing software interaction process are avoided. The software for evaluating the power generation capacity of the wind power plant based on the BS structure disclosed by the invention is more suitable for the wind power service in use, provides a data processing function in a service mode, is more flexible and convenient in deployment and use, can be used by a user independently, and can be integrated into service software of other BS frameworks to provide a data analysis function for other service software.
While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it will be understood by those of ordinary skill in the art that various changes in form and details may be made therein without departing from the spirit and scope of the present invention as defined by the following claims.

Claims (30)

1. A method for estimating power generation of a wind farm, the method comprising:
Acquiring a generated energy evaluation parameter file of a wind power plant, a power curve file of a wind generating set and parameters related to a wind resource loss item and an uncertainty item;
Calculating an overall loss value and an overall uncertainty value of the wind power plant based on the obtained generated energy evaluation parameter file, the obtained power curve file and parameters related to the loss item and the uncertainty item;
Establishing a generating capacity probability statistical model of the wind power plant according to the calculated overall loss value and the overall uncertainty value;
And calculating the power generation amount of the wind power plant under a specific transcendental probability by using the established power generation amount probability statistical model.
2. the method of claim 1,
The parameters related to the loss term comprise at least a parameter related to wake effects, a parameter related to availability, a parameter related to wind turbine performance, a parameter related to electrical, a parameter related to environment and a parameter related to curtailment;
the parameters related to the uncertainty term include at least parameters related to wind data, parameters related to a wind model, parameters related to power conversion, and parameters related to losses.
3. The method of claim 1, wherein the step of calculating an overall loss value and an overall uncertainty value for the wind farm comprises:
and calculating the integral loss value of the wind power plant by using a loss calculation model according to the acquired parameters related to the loss item and the wake flow influence parameters in the generated energy evaluation parameter file.
4. The method of claim 2, wherein calculating the overall loss value and the overall uncertainty value for the wind farm further comprises:
respectively calculating a wind speed level extrapolation uncertainty value of each machine site by using a wind speed level extrapolation uncertainty calculation model in combination with a trust coefficient according to the acquired parameters of the wind model, each machine site in the generated energy evaluation parameter file and the coordinate information of the wind measuring tower corresponding to each machine site;
And respectively calculating the wind speed vertical extrapolation uncertainty value of each machine site by using a wind speed vertical extrapolation uncertainty calculation model and combining a trust coefficient according to the acquired parameters of the wind model, the height information of each machine site and the wind measuring tower corresponding to each machine site in the generated energy evaluation parameter file and the hub height information of the wind generating set.
5. the method of claim 4, wherein the step of calculating the overall loss value and the overall uncertainty value for the wind farm further comprises:
Calculating a first uncertainty value for each machine site separately using a first uncertainty calculation model based on the acquired parameters for the wind data, the calculated wind speed horizontal and vertical extrapolation uncertainty values, and the sensitivity coefficient for each machine site,
an overall first uncertainty value for the wind farm is calculated using the first uncertainty value for each of the machine sites.
6. the method of claim 5, wherein the sensitivity coefficient of each machine location is calculated using a sensitivity coefficient calculation model based on the Weibull parameter A value, the K value, the average wind speed and the power curve of the wind turbine generator set corresponding to each machine location for each machine location in the power generation amount estimation parameter file, respectively.
7. The method of claim 5, wherein the step of calculating the overall loss value and the overall uncertainty value for the wind farm further comprises:
Respectively calculating a second uncertainty value of each machine point by using a second uncertainty calculation model according to the acquired parameters related to the power conversion;
Calculating an overall second uncertainty value for the wind farm using the second uncertainty value for each machine site;
respectively calculating a third uncertainty value of each machine point by using a third uncertainty calculation model according to the acquired parameters about the loss;
Calculating an overall third uncertainty value for the wind farm using the third uncertainty value for each machine site;
and calculating the integral uncertainty value of the wind power plant according to the calculated integral first uncertainty value, the integral second uncertainty value and the integral third uncertainty value.
8. The method of claim 7, wherein the step of establishing a probabilistic model of the power generation of the wind farm comprises:
and establishing a normal distribution model to obtain a probability distribution density function of the wind power plant based on the calculated overall loss value and the overall uncertainty value.
9. the method of claim 8, wherein the step of calculating the power generation of the wind farm at a particular transcendental probability comprises:
and calculating the power generation amount of the wind power plant under different transcendental probabilities by using a cumulative distribution function formed by the probability distribution density function.
10. An apparatus for estimating power generation of a wind farm, the apparatus comprising:
The data acquisition module is used for acquiring a generated energy evaluation parameter file of the wind power plant, a power curve file of the wind generating set and parameters related to a wind resource loss item and an uncertainty item;
the data calculation module is used for calculating an overall loss value and an overall uncertainty value of the wind power plant based on the acquired generated energy evaluation parameter file, the acquired power curve file and parameters related to the loss item and the uncertainty item;
And the model establishing module is used for establishing a power generation probability statistical model of the wind power plant according to the calculated overall loss value and the overall uncertainty value, and calculating the power generation amount of the wind power plant under the specific exceeding probability by using the established power generation probability statistical model.
11. the apparatus of claim 10, wherein the obtained parameters related to the loss term comprise at least a parameter related to wake effects, a parameter related to effectiveness, a parameter related to fan performance, a parameter related to electrical, a parameter related to environment, and a parameter related to curtailment; the parameters related to the uncertainty term obtained include at least parameters related to wind data, parameters related to a wind model, parameters related to power conversion, and parameters related to losses.
12. the apparatus of claim 10, wherein the data calculation module is further configured to calculate an overall loss value of the wind farm using the loss calculation model based on the obtained parameters related to the loss term and the wake effect parameters in the power generation evaluation parameter file.
13. The apparatus of claim 11, wherein the data calculation module is further to:
respectively calculating a wind speed level extrapolation uncertainty value of each machine site by using a wind speed level extrapolation uncertainty calculation model in combination with a trust coefficient according to the acquired parameters of the wind model, each machine site in the generated energy evaluation parameter file and the coordinate information of the wind measuring tower corresponding to each machine site;
And respectively calculating the wind speed vertical extrapolation uncertainty value of each machine site by using a wind speed vertical extrapolation uncertainty calculation model and combining a trust coefficient according to the acquired parameters of the wind model, the height information of each machine site and the wind measuring tower corresponding to each machine site in the generated energy evaluation parameter file and the hub height information of the wind generating set.
14. the apparatus of claim 13, wherein the data calculation module is further to:
Respectively calculating a first uncertainty value of each machine position point by using a first uncertainty calculation model according to the acquired parameters about the wind data, the calculated wind speed horizontal and vertical extrapolation uncertainty values and the sensitivity coefficient of each machine position point;
An overall first uncertainty value for the wind farm is calculated using the first uncertainty value for each of the machine sites.
15. The apparatus of claim 14, wherein the data calculation module is further to:
and respectively calculating the sensitivity coefficient of each machine position by using a sensitivity coefficient calculation model according to the Weibull parameter A value, the K value and the average wind speed of each machine position in the generated energy evaluation parameter file and the power curve of the wind generating set corresponding to each machine position.
16. The apparatus of claim 14, wherein the data calculation module is further to:
Respectively calculating a second uncertainty value of each machine point by using a second uncertainty calculation model according to the acquired parameters related to the power conversion;
Calculating an overall second uncertainty value for the wind farm using the second uncertainty value for each machine site;
Respectively calculating a third uncertainty value of each machine point by using a third uncertainty calculation model according to the acquired parameters about the loss;
Calculating an overall third uncertainty value for the wind farm using the third uncertainty value for each machine site;
and calculating the integral uncertainty value of the wind power plant according to the calculated integral first uncertainty value, the integral second uncertainty value and the integral third uncertainty value.
17. the apparatus of claim 16, wherein the model building module is to:
and establishing a normal distribution model to obtain a probability distribution density function of the wind power plant based on the calculated overall loss value and the overall uncertainty value.
18. The apparatus of claim 17, wherein the model building module is further configured to:
And calculating the power generation amount of the wind power plant under different transcendental probabilities by using a cumulative distribution function formed by the probability distribution density function.
19. A system for estimating power generation of a wind farm, the system comprising:
a browser configured to input and display data;
A server configured to:
Receiving a generated energy evaluation parameter file of a wind power plant, a power curve file of a wind generating set and parameters related to a wind resource loss item and an uncertainty item through a browser;
Calculating an overall loss value and an overall uncertainty value of the wind farm based on the received power generation amount evaluation parameter file, the power curve file, and parameters related to the wind resource loss term and the uncertainty term;
Establishing a generating capacity probability statistical model of the wind power plant according to the calculated overall loss value and the overall uncertainty value;
and calculating the power generation amount of the wind power plant under a specific transcendental probability by using the established power generation amount probability statistical model.
20. the system of claim 19, wherein the received parameters related to the loss term include at least a parameter related to wake effects, a parameter related to effectiveness, a parameter related to fan performance, a parameter related to electrical, a parameter related to environmental, and a parameter related to curtailment; the received parameters related to the uncertainty term include at least parameters related to wind data, parameters related to a wind model, parameters related to power conversion, and parameters related to losses.
21. The system of claim 19, wherein the server is further configured to calculate an overall loss value for the wind farm using the loss calculation model based on the received parameters related to the loss term, the wake effect parameters in the power generation evaluation parameter file.
22. the system of claim 20, wherein the server is further configured to:
Respectively calculating a wind speed level extrapolation uncertainty value of each machine site by using a wind speed level extrapolation uncertainty calculation model in combination with a trust coefficient according to the received parameters about the wind model, each machine site in the generated energy evaluation parameter file and the coordinate information of the wind measuring tower corresponding to each machine site;
and respectively calculating a wind speed vertical extrapolation uncertainty value of each machine site by using a wind speed vertical extrapolation uncertainty calculation model and combining a trust coefficient according to the received parameters about the wind model, the height information of each machine site and the wind measuring tower corresponding to each machine site in the generated energy evaluation parameter file and the hub height information of the wind generating set.
23. The system of claim 22, wherein the server is further configured to:
Calculating a first uncertainty value for each machine point using a first uncertainty calculation model based on the received parameters about the wind data, the calculated wind speed horizontal and vertical extrapolation uncertainty values, and the sensitivity coefficient for each machine point, respectively;
An overall first uncertainty value for the wind farm is calculated using the first uncertainty value for each of the machine sites.
24. The system of claim 23, wherein the server is further configured to:
and respectively calculating the sensitivity coefficient of each machine position by using a sensitivity coefficient calculation model according to the Weibull parameter A value, the K value and the average wind speed of each machine position in the generated energy evaluation parameter file and the power curve of the wind generating set corresponding to each machine position.
25. The system of claim 23, wherein the server is further configured to:
calculating a second uncertainty value for each machine point using a second uncertainty calculation model based on the received parameters for the power conversion;
calculating an overall second uncertainty value for the wind farm using the second uncertainty value for each machine site;
calculating a third uncertainty value for each machine point using a third uncertainty calculation model based on the received loss-related parameters;
calculating an overall third uncertainty value for the wind farm using the third uncertainty value for each machine site;
And calculating the integral uncertainty value of the wind power plant according to the calculated integral first uncertainty value, the integral second uncertainty value and the integral third uncertainty value.
26. The system of claim 25, wherein the server is further configured to:
And establishing a normal distribution model to obtain a probability distribution density function of the wind power plant based on the calculated integral loss value and integral uncertainty value of the wind power plant.
27. the system of claim 26, wherein the server is further configured to:
And calculating the power generation amount of the wind power plant under different transcendental probabilities by using a cumulative distribution function formed by the probability distribution density function.
28. the system of claim 19, wherein the server is further configured to:
generating a power generation estimate report related to the loss term and the uncertainty term using the calculated overall loss value, the overall uncertainty value, and the power generation at different transcendental probabilities, or
And sending the calculated overall loss value, the overall uncertainty value and the generated energy under different overrun probabilities to a browser, so that the browser displays the overall loss value, the overall uncertainty value and the generated energy under different overrun probabilities.
29. a computer-readable storage medium storing a program, the program comprising instructions for performing the method of any one of claims 1-9.
30. A computer comprising a readable medium having a computer program stored thereon, wherein the computer program comprises instructions for performing the method according to any one of claims 1-9.
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