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‎docs/source/index.md‎

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# Simulating two-axis tracking solar collectors
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twoaxistracking is a python package for simulating the performance of two-axis tracking solar collectors, particularly for calculating self-shading.
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`twoaxistracking` is a python package for simulating two-axis tracking solar collectors, particularly self-shading.
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For a quick introduction to the package, check out the [intro tutorial](../notebooks/intro_tutorial) which demonstrates the main functionality. For further details, check out the [code documentation](../documentation).
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‎docs/source/installation.md‎

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conda install shapely
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The solar modeling library `pvlib` is recommended for calculating the solar position and can be installed by the command:
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The solar energy modeling library `pvlib` is recommended for calculating the solar position and can be installed by the command:
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pip install pvlib

‎docs/source/notebooks/field_layout_discretization.ipynb‎

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"metadata": {},
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"## Visualization of layout discretization\n",
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"The following plots illustrate the discrete aspect ratios and offsets for GCR=0.2 and GCR=0.3. Note, each point shown is simulated for the all discrete rotations."
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"The following plot illustrates the discrete aspect ratios and offsets for GCR=0.2 and GCR=0.3. Note, each point shown is simulated for the all discrete rotations."
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.5"
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"version": "3.7.11"
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}
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"nbformat": 4,

‎docs/source/notebooks/reference_dataset.ipynb‎

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"metadata": {},
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"## Load the dataset\n",
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"The original dataset is a fixed-width-file (fwf), whih can be conveniently parsed using the pandas `read_fwf` function:"
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"The original dataset is a fixed-width-file (fwf), which can be conveniently parsed using the pandas `read_fwf` function:"
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{
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"cell_type": "code",
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"execution_count": 3,
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"4 76 1 1 1 15 0.88 0.00 -2.78"
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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"source": [
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"data_url = 'https://www.nrel.gov/grid/solar-resource/assets/data/f06.txt'\n",
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"\n",
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"# Read file as a fixed-with-file (fwf)\n",
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"# Read file\n",
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"df_barstow = pd.read_fwf(data_url, skiprows=[0,2])\n",
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"# Only keep data from 1976\n",
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"<br>\n",
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"Next, we set the index to datetime:"
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"Then, we set the index to datetime:"
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"execution_count": 4,
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"1976-01-01 01:15:00-08:00 0.88 0.00 -2.78"
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"execution_count": 4,
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"metadata": {},
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"# This step requires defining a custom function (*convert_to_datetime*) for converting\n",
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"# the timestamps to datetime, as the time timestamps do not following the ISO convention\n",
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"# This step requires defining a custom function (convert_to_datetime) for converting\n",
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"# the timestamps to datetime, as the time timestamps do not follow the ISO convention\n",
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"# for denoting midnight as 00:00, but rather represents midnight as 24:00.\n",
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"def convert_to_datetime(time):\n",
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" \"\"\"Convert string with instances of midnight as\n",
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"Next, the columns are renamed in order to conform to the standard pvlib names:"
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"Next, the columns are renamed in order to conform to the [standard pvlib names](https://pvlib-python.readthedocs.io/en/stable/variables_style_rules.html):"
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"1976-01-01 01:15:00-08:00 0.88 0.00 -2.78"
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"1976-01-01 01:15:00-08:00 -3.212865 1413.981805 "
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"## Removal of erroneous data\n",
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"The removal of erroneous data has been done by manually inspecting each day.\n",
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"The identification of erroneous data has been done by manually inspecting each day.\n",
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"The identified erroneous values are set to nan:"
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"source": [
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"df['dni_inplane'] = (df['dni'] * np.cos(np.deg2rad(df['apparent_zenith']))).clip(lower=0)\n",
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"df['dhi'] = (df['ghi'] - df['dni_inplane']).clip(lower=0)\n",
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"# Calculate beam/direct horizontal irradiance (BHI)\n",
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"df['bhi'] = (df['dni'] * np.cos(np.deg2rad(df['apparent_zenith']))).clip(lower=0)\n",
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"# Calculate diffuse horizontal irradiance (DHI)\n",
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"df['dhi'] = (df['ghi'] - df['bhi']).clip(lower=0)\n",
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"df['Kn'] = df['dni'] / df['dni_extra']\n",
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"In order to detect possible shading from nearby objects and hills, it is useful to visualize the maximum irradiance as a function of solar elevation and zenith angle. Particularly the DNI plot is helpful in identifiying the local obstruction free horizon line.\n",
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"Due to the 15-minute resolution a 2-degree angle resolution is used."
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"Due to the 15-minute resolution a 2-degree resolution is used."
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"### Quality-check plots"
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"### Quality-check plots\n",
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"The below plots visualize the normalize irradiance values. The blue lines represents the [quality-control limits recommend by the BSRN](https://epic.awi.de/id/eprint/30083/1/BSRN_recommended_QC_tests_V2.pdf)."
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"dtype: object"
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"Clearly, the DNI measurements are errornous to a much higher degree than the GHI measurements. This can partly be due to the fact that it is much easier to detect a faulty DNI measurements, and also that the DNI sensor is much more prone to soiling and is affected by tracker errors and misalignment."
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"Clearly, the DNI measurements are errornous to a much higher degree than the GHI measurements. This is partly due to the fact that it is much easier to detect a faulty DNI measurement, but also that DNI sensors are much more prone to soiling and affected by tracker errors and misalignment."
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