SolarPosition | Javascript Sunrise/Set calculator | Apps library

I have a data that 1-dimensional float64 data that contain date and value as like this:

I'm getting a dataset with UTC data, and coordinates lat,long I want to compute the solarposition for each of the row of this dateset, but I'm having trouble with manipulating the timezone.

So far, I've managed to make the UTC data, timezone aware by:

I am modeling a PV array using PVlib and sometimes when I am trying to access the weather forecast data I get the following error:

Background

Traditionally I've used NREL SAM tool to estimate solar output. I've been experimenting with PVLIB which is great due to the open nature and flexibility, however I can't seem to reconcile the solar production estimates between PVLIB and NREL SAM.

What I've done

I'm modeling a hypothetical solar farm near Gympie QLD. I've gone to the climate.onebuiling website, and downloaded the zip folder / epw file for "AUS_QLD_Gympie.AP.945660_TMYx.2003-2017". I've then used that weather file in NREL's SAM tool using PVwatts, with the following specs;

• 200,000 KWdc
• Module Type = Standard
• 1.2 DC to AC Ratio
• 96% inverter efficiency
• 1 axis backtracking
• tilt = 26 degrees
• azimuth = 0 degrees
• GCR = 0.4
• losses, shading & curtailment = default

In NREL SAM i get an annual Energy Yield (AC GWh) of 415.96 GWh p.a.

I then took that same epw file and converted it to a csv, keeping just the columns for ghi, dni, dhi, temp_air & wind_speed (Google Drive link to CSV file). I've used this file as an import into PVLIB. I spec'd a PVLIB system the same specs above, with addition of albedo = 0.2 and max angle = 90 degrees (Code Below).

The result I get in PVLIB is 395.61 GWh.

Problem

The results I got are pretty different. PVLIB = ~395 GWh p.a. vs SAM = ~415 GWH p.a. I expected around a 1-2% difference, but not 5%.

Figures are even worse when I compare to a PVLIB system using clearsky.ineichen (adjusted with linke_turbidity) which yields ~475 GWh p.a.

Help Requested

Anyone know why my results are so different? is there anything I can do to narrow the gap?

PVLIB Code

I need calculate the solar zenith angle for approximately 106.000.000 of different coordinates. This coordinates are referrals to the pixels from an image projected at Earth Surface after the image had been taken by camera into the airplane.

I am using the pvlib.solarposition.get_position() to calculate the solar zenith angle. The values returned are being calculated correctly (I compared some results with NOOA website) but, how I need calculate the solar zenith angle for many couple of coordinates, the python is spending many days (about 5 days) to finish the execution of the function.

How I am a beginner in programming, I wonder is there is any way to accelerate the solar zenith angle calculation.

Below found the part of the code implemented which calculate the solar zenith angle:

I am trying to build a generic pvsystem for modelling without the need for specific modules and inverters. I have all the key variables (surface azimuth, tilt, albedo, tracking technology(Fixed, Single, Dual), location, DC size in KW, loss factors, DC/AC ratio)

I understand PVWatts is the best way to do this, but I don't know how it works. how best would I build such a pvsystem in PVLIB?

Current code attached, It works but looking to change the pvsystem build

I'm trying to calculate the hour angle for using the PVLIB's function "pvl.solarposition.hour_angle()".

The code that I'm developing is structured in 02 parts:

• First I'm transforming GPS time (seconds of week) in UTC Time formated in '%Y-%m-%d %H:%M:%S'. The result obtained here is in UTC time because the GPS time is refering in UTC.
• On second part I try to calculate the hour angle using PVLIB and on this part I'm having problems.

When I'll run the hour angle the python is returning the an error message:

"naive_times = times.tz_localize(None) # naive but still localized AttributeError: 'str' object has no attribute 'tz_localize".

I know that this erros is about the class for variable "final_time" on the code implemented. According the PVLIB documentation this variable is need stay on class pandas.DatetimeIndex (https://pvlib-python.readthedocs.io/en/latest/generated/pvlib.solarposition.hour_angle.html) and I dont know transform correctly the GPS time to this class and then, use this result on the PVLIB to compute the hour angle.

Bellow follow part of algorithm that I'm using here on the tests:

While looking at the code for the calculations of extraterrestrial radiation get_extra_radiation() and crosschecking it with literature, I noticed that for both methods 'asce' and 'spencer' the solarposition._calculate_simple_day_angle(doy) function is used. This function basically just calculates (2. * np.pi / 365.) * (doy- 1), which is correct for method='spencer'.

But I think for method='asce' it should just be (2. * np.pi * doy / 365.) - without "- 1" - as described on page 9 in "J. A. Duffie and W. A. Beckman, "Solar Engineering of Thermal Processes, 3rd Edition" J. Wiley and Sons, New York (2006)"

Dear pvlib users and devels.

I'm a researcher in computer science, not particularly expert in the simulation or modelling of solar panels. I'm interested in use pvlib since we are trying to simulate the works of a small solar panel used for IoT applications, in particular the panel spec are the following: 12.8% max efficiency, Vmp = 5.82V, size = 225 × 155 × 17 mm.

Before using pvlib, one of my collaborator wrote a code that compute the irradiation directly from average monthly values calculated with PVWatt. I was not really satisfied, so we are starting to use pvlib. In the old code, we have the power and current of the panel calculated as:

W = Irradiation * PanelSize(m^2) * Efficiency A = W / Vmp

The Irradiation, in Madrid, as been obtained with PVWatt, and this is what my collaborator used: DIrradiance = (2030.0,2960.0,4290.0,5110.0,5950.0,7090.0,7200.0,6340.0,4870.0,3130.0,2130.0,1700.0) I'm trying to understand if pvlib compute values similar to the ones above, as averages over a day for each month. And the curve of production in day. I wrote this to compare pvlib with our old model: