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2023 pvlib python user meeting notes

Kevin Anderson edited this page Oct 13, 2023 · 4 revisions

At the 2023 PVPMC, we held a pvlib-python user's group meeting. We collected ideas for new features and examples for pvlib-python. Here are the results with upvotes (+) as indicated by those participating in the user's meeting.

The notes are organized as checkboxes in the hope that some of these items can be addressed through code contributions.

Some of the Examples also suggest the need for improved models.

Modeling enhancements:

  • Simple LCOE calculation based on scalars and escalators.
  • Horizon shading effects.
    • PR #1395 added a function to retrieve PVGIS horizon profiles
  • Shade modeling (consider GPU acceleration).
  • Add uncertainty propagation and analysis.
  • Models for tracker dynamics (e.g. effects of intermittent tracker movements).
  • Conversion of POA global irradiance to effective irradiance (accounts for reflections and spectrum), or decompose POA global to components.
  • Simple model for inverter power factor
  • Models for off-maximum power point (MPP), clipping and curtailment (+)
  • Account for inverter MPPT voltage range (+)

For iotools:

  • Fetch precipitation data for a location. ++
    • PR #1767 added functions to fetch daily PRISM, NRCC, and ground station precipitation data
  • Import PAN and OND files (these are the Pvsyst files for modules and inverters). ++
    • PR #1749 added functions to parse PAN & OND files into python data structures
  • Download AOD data

For ivtools:

  • Functions for cell and module mismatch calculations

Data manipulation:

  • Fill gaps in data.

Examples:

  • Effects of far shading on beam and diffuse irradiance.
    • PR #1849 added a gallery example for simple DNI adjustment for far shading
  • Improve and update inverter models.
  • Simulate microinverters with accurate handling of clipping, DC current and DC voltage limits.
  • Modeling of short term losses (LID, mismatch, ...)
  • Model near shading (linear losses and detailed losses).
  • Model a repowering scenario, where a system's modules are replaced.
  • Connect pvlib-python with PySAM's financial calculations.