Free Turbine Points Code Generator
Agathe Thies
What I'm interested is to find a wind power profile (windDG1) for this turbine. And my simulation length is 24 hours. Can anyone give me some suggestions that where to get the appropriate wind power profile or maybe PV profile that I could use to simulate those DGs in a microgrid environment?
I think the longest PV shape that we provide as part of the OpenDSS distribution (in the examples folder under load-shapes) is 2900 seconds long. I don't know if NREL has any publicly accessible data, but you might try a google search with the key-words: time series PV NREL or maybe time series solar NREL.
We regularly run simulations of over 30,000 points in 1-s intervals, which covers the daylight hours. We also use 1-min data and longer intervals. EPRI does not provide these data -- only the 2900-s shape cited in the previous post -- but there are many folks out there who now have tons of data. Besides NREL, check Sandia (SNL) also. Depending on where you are, your local utility may have solar PV data that you can use. Readers, if you have public data, please post it somewhere and give us a link.
I've been using data from -based-station-data/solar-radiation
It is mostly modelled (i.e. not real measured data) and in hourly format, which, for simulation purposes, can be interpolated to get higher resolution.
If your modeled data is at hourly resolution, you will miss much of the variability of PV that occurs at much shorter time frames. However, depending on your needs, hourly resolution may be sufficient.
Certainly, linear interpolation will not recover actual data. I'm running a rough simulation to compare demand and PV output across a year with half-hourly data, so interpolation is only used to match the resolution (i.e. size) of data sets.
The power electronics on a wind turbine are sized to only produce a certain maximum power, called the rated power of the turbine (1.5 MW for this turbine). The rated torque, wind speed, and rotor speed correspond to the operating conditions where the turbine achvies the rated power.
Region 3 (blade pitch control): For wind speeds above rated, the generator torque is set to its rated value while the blade pitch is adjusted to maintain the rated rotor speed and deliver the rated power.
The generator torque in Region 2 is set to GenTRated = Kreg2wRatedLSS^2, where you choose Kreg2 such that there is a smooth transition with Region 3. The rotor speed is wRatedLSS and generator torque is GenTRated.
In Region 3 (blade pitch control), the rotor speed and generator torque max out to their rated values wRatedLSS and GenTRated, respectively. Use findop to compute the blade pitch angle that maintains these steady-state values.
Plot the optimal settings of rotor speed, generator torque, electric power, and blade pitch angle as a function of wind speed. The red dot marks the rated operating point and transition between Regions 2 and 3.
Design a gain-scheduled PI controller to adjust blade pitch in Region 3. The gains are scheduled on the blade pitch angle, which you can measure more reliably than wind speed. Recall that blade pitch must be adjusted to keep rotor speed, generator torque, and electric power from exceeding their rated values.
From the LPV model Glpv of the turbine and the PI gain schedule, you can also construct a closed-loop LPV model and use it to validate the gain-scheduled controller in Region 3. First use ssInterpolant to create an LPV model of the gain-scheduled controller.
The LPV simulation accurately models rotor speed and blade pitch but underestimates the drop in power when wind speed decreases. This is because the LPV simulation fails to account for the drop in generator torque from the relationship GenTRated = Kreg2wRatedLSS^2.
In conjunction with the development of the Wind Energy Engineering course notes, a number of computer codes have been assembled and made accessible from a simple graphical user interface. In most cases, the codes apply methods that are discussed in the course notes. In some cases the codes embody techniques that are beyond the scope of the notes, but they are included because they might nonetheless be of use to the student or practicing engineer.
The codes were written in Microsoft Visual Basic, Ver. 3.0. They can be used on any Windows XP PC (not Macs, unfortunately). Please note that these codes will not work on Windows 7 (and later) PCs without additional files, the most important of which is the MS Visual Basic runtime dll: -us/kb/180071.
Below are summaries of the capabilities of each of the codes. Each summary gives an overview of the purpose and function of the code and describes the form of the inputs and outputs. A brief description of the methods employed underlying algorithms is also included. Finally, some tests for verifying the accuracy codes are discussed.
Function: This procedure may be used to find the basic statistical characteristics of a data file. These include mean, standard deviation, maximum, minimum and number of points. Inputs The input is a text file, with one point per line. Outputs The output appears in text boxes on the screen.
Validation: Select a bin width and create a file for which the number of occurrences in each bin over the range of the file may be readily determined. The output should show the same results.
Validation: Hand calculations of the c and k values using techniques of Chapter 2 should give the same results as the code. A probability density function may be generated using the output values and superimposed upon a normalized histogram of the input file. The match should be fairly close.
Function: This routine may be used to perform a crosscorrelation analysis between two time series data files. The averages are removed from the data before the analysis. Crosscorrelation analysis is often used to when comparing wind data taken at two different locations.
Function: This procedure is used to block average a data file, thereby increasing the effective averaging time and decreasing the total number of points. For example, a typical application is to reduce a data set of 1 minute averages of wind speed to hourly averages.
Outputs: The output is text file, with one point per line. The number of points is reduced from that of the original by the ratio of the original time step divided by the desired time step.
Validation: A simple test involves comparing the average of a block averaged output data file with an input file. The averages should be the same. The standard deviation is generally somewhat smaller. Comparing histograms should result in similar histograms, with generally less spread.
Outputs: The output is to a comma delimited text file, 2 points per line: frequency and power spectral density (units squared)/frequency unit. The number of lines will be equal to the segment length divided by two.
Methods: The algorithms used employ the Fast Fourier Transform method, and conversion of the Fourier Transform to the one side power spectral density. Details are provided in Bendat and Piersol (1986).
Validation: A simple test is to generate a sine wave consisting of multiple cycles. A peak should appear at frequencies close to the frequency of the sine wave. The sum of all the psd terms, times the difference between any two frequencies, should equal the variance of the input file.
Validation: A synthesized file of can be tested with the Statistics of File, the Histogram of a File, and the Autocorrelation. The results should confirm that the file has the desired characteristics.
Function: This procedure may be used to derive a Markov process Transition Probability Matrix from a data time series. This matrix can then be used (see below) to generate a time series with the same mean, standard deviation, and probability density function as that of the original data. The autocorrelation will be decrease exponentially, but will be close to that of the original data for low lag numbers.
Outputs: The output is saved to a comma delimited text file, representing a square matrix of size N x N, augmented by a column at the front which specifies the mean of the bin. Subsequent entries in each row indicate the probability of making a transition from the bin corresponding to the row to the bin (bin N) corresponding to the column (bin N-1).
Validation: The TPM may be checked indirectly, by first using it to synthesize a data file, as described below. The synthesized file can be tested with the Statistics of File, the Histogram of a File, and the Autocorrelation. The results should confirm that the file has the expected characteristics.
Function: This procedure can be used to generate synthetic hourly wind speed data. It uses a Markov process method that results in a time series with a specified mean, standard deviation, probability density function (Rayleigh or Weibull), and autocorrelation. A diurnal sinusoidal variation, starting at a specified hour, may also be imposed.
Inputs: The inputs are made on the screen. They include the desired mean, standard deviation, type of probability density function (Rayleigh or Weibull), autocorrelation and corresponding lag. The diurnal characteristics are input by specifying i) the ratio between the maximum diurnal value and the average value and ii) the time of the maximum.
Validation: The method can be tested by first synthesizing a time series. The synthesized file can be tested with the Statistics of File, the Histogram of a File, and the Autocorrelation. The results should confirm that the file has the desired characteristics.
Function: This procedure can be used to generate synthetic hourly load data. It uses a Markov process method that results in a time series with a specified mean, standard deviation, probability density function (shifted Rayleigh), and autocorrelation. A diurnal sinusoidal variation, starting at a specified hour, may also be imposed.
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