9 - Advanced topics - 1 axis torque tube Shading for 1 day (Research documentation).py

#!/usr/bin/env python
# coding: utf-8

# # 9 - Advanced topics - 1 axis torque tube Shading for 1 day (Research Documentation)
# ## Recreating JPV 2019 / PVSC 2018 Fig. 13
# 
# 
# Calculating and plotting shading from torque tube on 1-axis tracking for 1 day, which is figure 13 in: 
# 
#             Ayala Pelaez S, Deline C, Greenberg P, Stein JS, Kostuk RK. Model and validation of single-axis tracking with bifacial PV. IEEE J Photovoltaics. 2019;9(3):715–21. https://ieeexplore.ieee.org/document/8644027 and https://www.nrel.gov/docs/fy19osti/72039.pdf (pre-print, conference version)
# 
# 
# This is what we will re-create:
# ![Ayala JPV-2](../images_wiki/JPV_Ayala_Fig13.png)
# 
# Use bifacial_radiance minimum v. 0.3.1 or higher. Many things have been updated since this paper, simplifying the generation of this plot:
# 
# <ul>
#     <li> Sensor position is now always generated E to W on N-S tracking systems, so same sensor positions can just be added for this calculation at the end without needing to flip the sensors. </li>
#     <li> Torquetubes get automatically generated in makeModule. Following PVSC 2018 paper, rotation is around the modules and not around the torque tube axis (which is a new feature) </li>
#     <li> Simulating only 1 day on single-axis tracking easier with cumulativesky = False and gendaylit1axis(startdate='06/24', enddate='06/24' </li> 
#     <li> Sensors get generated very close to surface, so all results are from the module surface and not the torquetube for this 1-UP case. </li>
# </ul>
# 
# ## Steps:
# 
# <ol>
#     <li> <a href='#step1'> Running the simulations for all the cases: </li>
#     <ol type='A'> 
#         <li> <a href='#step1a'>Baseline Case: No Torque Tube </a></li>
#         <li> <a href='#step1b'> Zgap = 0.1 </a></li>
#         <li> <a href='#step1c'> Zgap = 0.2 </a></li>
#         <li> <a href='#step1d'> Zgap = 0.3 </a></li>
#     </ol>
#     <li> <a href='#step2'> Read-back the values and tabulate average values for unshaded, 10cm gap and 30cm gap </a></li>
#     <li> <a href='#step3'> Plot spatial loss values for 10cm and 30cm data </a></li>
#     <li> <a href='#step4'> Overall Shading Factor (for 1 day) </a></li>
# </ol>
# 
# 

# <a id='step1'></a>

# ### 1. Running the simulations for all the cases

# In[1]:


import os
testfolder = os.path.abspath(r'..\bifacial_radiance\TEMP')  

# You can alternatively point to an empty directory (it will open a load GUI Visual Interface)
# or specify any other directory in your computer. I.E.:
# testfolder = r'C:\Users\sayala\Documents\RadianceScenes\Demo'

print ("Your simulation will be stored in %s" % testfolder)


# In[2]:


# VARIABLES of the simulation: 
lat = 35.1 # ABQ
lon = -106.7 # ABQ
x=1
y = 2 
numpanels=1
limit_angle = 45 # tracker rotation limit angle
albedo = 'concrete'     # ground albedo
hub_height = y*0.75   # H = 0.75 
gcr = 0.35  
pitch = y/gcr
#pitch = 1.0/gcr # Check from 1Axis_Shading_PVSC2018 file
cumulativesky = False # needed for set1axis and makeScene1axis so simulation is done hourly not with gencumsky.
limit_angle = 45 # tracker rotation limit angle
nMods=10
nRows=3
sensorsy = 200
module_type='2m_panel'
datewanted='06/24' # sunny day 6/24/1972 (index 4180 - 4195)

## Torque tube info
torquetube = False # redefined on each simulation below, since we need to compare with and without torque tube.
tubetype='round'
material = 'Metal_Grey'
diameter = 0.1
axisofrotationTorqueTube = False   # Original PVSC version rotated around the modules like most other software.
# Variables that will get defined on each iteration below:
zgap = 0 # 0.2, 0.3 values tested. Re-defined on each simulation.
torquetube = False # baseline is no torque tube. 


# In[3]:


# Simulation Start.

try:
    import bifacial_radiance
except ImportError:
    raise RuntimeError('bifacial_radiance is required. download distribution')
import numpy as np

print(bifacial_radiance.__version__)

demo = bifacial_radiance.RadianceObj(path = testfolder)  
demo.setGround(albedo)
epwfile = demo.getEPW(lat, lon) 
metdata = demo.readEPW(epwfile) 
trackerdict = demo.set1axis(metdata, limit_angle = limit_angle, backtrack = True, gcr = gcr, cumulativesky = False)
trackerdict = demo.gendaylit1axis(startdate=datewanted, enddate=datewanted) 
sceneDict = {'pitch':pitch,'hub_height':hub_height, 'nMods': nMods, 'nRows': nRows}  


# <a id='step1a'></a>

# ### A. Baseline Case: No Torque Tube
# 
# When torquetube is False, zgap is the distance from axis of torque tube to module surface, but since we are rotating from the module's axis, this Zgap doesn't matter for this baseline case.

# In[6]:


#CASE 0 No torque tube
# When torquetube is False, zgap is the distance from axis of torque tube to module surface, but since we are rotating from the module's axis, this Zgap doesn't matter.
# zgap = 0.1 + diameter/2.0  
torquetube = False 
customname = '_NoTT'
demo.makeModule(name=module_type,x=x,y=y, numpanels=numpanels, torquetube=torquetube, axisofrotationTorqueTube=axisofrotationTorqueTube)
trackerdict = demo.makeScene1axis(trackerdict,module_type,sceneDict, cumulativesky = cumulativesky) 
trackerdict = demo.makeOct1axis(trackerdict)
trackerdict = demo.analysis1axis(trackerdict, sensorsy = sensorsy, customname = customname)


# <a id='step1b'></a>

# ### B. ZGAP = 0.1

# In[7]:


#ZGAP 0.1 
zgap = 0.1
torquetube = True
customname = '_zgap0.1'
demo.makeModule(name=module_type,x=x,y=y, numpanels=numpanels,tubetype=tubetype, zgap=zgap, torquetube=torquetube, diameter=diameter, material=material, axisofrotationTorqueTube=axisofrotationTorqueTube)
trackerdict = demo.makeScene1axis(trackerdict,module_type,sceneDict, cumulativesky = cumulativesky) 
trackerdict = demo.makeOct1axis(trackerdict)
trackerdict = demo.analysis1axis(trackerdict, sensorsy = sensorsy, customname = customname)


# <a id='step1c'></a>

# ### C. ZGAP = 0.2

# In[8]:


#ZGAP 0.2
zgap = 0.2
torquetube = True
customname = '_zgap0.2'
demo.makeModule(name=module_type,x=x,y=y, numpanels=numpanels,tubetype=tubetype, zgap=zgap, torquetube=torquetube, diameter=diameter, material=material, axisofrotationTorqueTube=axisofrotationTorqueTube)
trackerdict = demo.makeScene1axis(trackerdict,module_type,sceneDict, cumulativesky = cumulativesky) 
trackerdict = demo.makeOct1axis(trackerdict)
trackerdict = demo.analysis1axis(trackerdict, sensorsy = sensorsy, customname = customname)


# <a id='step1d'></a>

# ### D. ZGAP = 0.3

# In[9]:


#ZGAP 0.3
zgap = 0.3
torquetube = True
customname = '_zgap0.3'
demo.makeModule(name=module_type,x=x,y=y, numpanels=numpanels,tubetype=tubetype, zgap=zgap, torquetube=torquetube, diameter=diameter, material=material, axisofrotationTorqueTube=axisofrotationTorqueTube)
trackerdict = demo.makeScene1axis(trackerdict,module_type,sceneDict, cumulativesky = cumulativesky) 
trackerdict = demo.makeOct1axis(trackerdict)
trackerdict = demo.analysis1axis(trackerdict, sensorsy = sensorsy, customname = customname)


# <a id='step2'></a>

# ### 2. Read-back the values and tabulate average values for unshaded, 10cm gap and 30cm gap
# 

# In[7]:


import glob
import pandas as pd

resultsfolder = os.path.join(testfolder, 'results')
print (resultsfolder)
filenames = glob.glob(os.path.join(resultsfolder,'*.csv'))
noTTlist = [k for k in filenames if 'NoTT' in k]
zgap10cmlist = [k for k in filenames if 'zgap0.1' in k]
zgap20cmlist = [k for k in filenames if 'zgap0.2' in k]
zgap30cmlist = [k for k in filenames if 'zgap0.3' in k]

# sum across all hours for each case
unsh_front = np.array([pd.read_csv(f, engine='python')['Wm2Front'] for f in noTTlist]).sum(axis = 0)
cm10_front = np.array([pd.read_csv(f, engine='python')['Wm2Front'] for f in zgap10cmlist]).sum(axis = 0)
cm20_front = np.array([pd.read_csv(f, engine='python')['Wm2Front'] for f in zgap20cmlist]).sum(axis = 0)
cm30_front = np.array([pd.read_csv(f, engine='python')['Wm2Front'] for f in zgap30cmlist]).sum(axis = 0)
unsh_back = np.array([pd.read_csv(f, engine='python')['Wm2Back'] for f in noTTlist]).sum(axis = 0)
cm10_back = np.array([pd.read_csv(f, engine='python')['Wm2Back'] for f in zgap10cmlist]).sum(axis = 0)
cm20_back = np.array([pd.read_csv(f, engine='python')['Wm2Back'] for f in zgap20cmlist]).sum(axis = 0)
cm30_back = np.array([pd.read_csv(f, engine='python')['Wm2Back'] for f in zgap30cmlist]).sum(axis = 0)


# <a id='step3'></a>

# ### 3. plot spatial loss values for 10cm and 30cm data

# In[5]:


import matplotlib.pyplot as plt
plt.rcParams['font.family'] = 'sans-serif'
plt.rcParams['font.sans-serif'] = ['Helvetica']
plt.rcParams['axes.linewidth'] = 0.2 #set the value globally

fig = plt.figure()
fig.set_size_inches(4, 2.5)
ax = fig.add_axes((0.15,0.15,0.78,0.75))
#plt.rc('font', family='sans-serif')
plt.rc('xtick',labelsize=8)
plt.rc('ytick',labelsize=8)
plt.rc('axes',labelsize=8)
plt.plot(np.linspace(-1,1,unsh_back.__len__()),(cm30_back - unsh_back)/unsh_back*100, label = '30cm gap',color = 'black')  #steelblue
plt.plot(np.linspace(-1,1,unsh_back.__len__()),(cm20_back - unsh_back)/unsh_back*100, label = '20cm gap',color = 'steelblue', linestyle = '--')  #steelblue
plt.plot(np.linspace(-1,1,unsh_back.__len__()),(cm10_back - unsh_back)/unsh_back*100, label = '10cm gap',color = 'darkorange')  #steelblue
#plt.ylabel('$G_{rear}$ vs unshaded [Wm-2]')#(r'$BG_E$ [%]')
plt.ylabel('$G_{rear}$ / $G_{rear,tubeless}$ -1 [%]')
plt.xlabel('Module X position [m]')
plt.legend(fontsize = 8,frameon = False,loc='best')
#plt.ylim([0, 15])
plt.title('Torque tube shading loss',fontsize=9)
#plt.annotate('South',xy=(-10,9.5),fontsize = 8); plt.annotate('North',xy=(8,9.5),fontsize = 8)
plt.show()


# <a id='step4'></a>

# ### 4. Overall Shading Loss Factor
# 
# To calculate shading loss factor, we can use the following equation:
# 
# 
# 
# <img src="../images_wiki/AdvancedJournals/Equation_ShadingFactor.png">

# In[ ]:


ShadingFactor = (1 - cm30_back.sum() / unsh_back.sum())*100