Quick Overview

Here is a quick overview of PyWAsP. To begin with, lets import pywasp, windkit. We will use pathlib.Path to point to the data location.

In [1]: from pathlib import Path

In [2]: import pywasp as pw

In [3]: import windkit as wk

In [4]: path = Path("../modules/examples/tutorial_4/data")

Read in a observed wind climate

You can use windkit to open a WAsP .omwc file, which is a binned observed wind climate file, and reproject it to UTM32 coordinates

In [5]: bwc = wk.read_bwc(path / "SerraSantaLuzia.omwc", crs="EPSG:4326")

In [6]: bwc = wk.spatial.reproject(bwc, to_crs="EPSG:32629")

In [7]: print(bwc)
<xarray.Dataset>
Dimensions:       (point: 1, sector: 12, wsbin: 32)
Coordinates:
    height        (point) float64 25.3
    crs           int8 0
  * wsbin         (wsbin) float64 0.5 1.5 2.5 3.5 4.5 ... 28.5 29.5 30.5 31.5
    wsceil        (wsbin) float64 1.0 2.0 3.0 4.0 5.0 ... 29.0 30.0 31.0 32.0
    wsfloor       (wsbin) float64 0.0 1.0 2.0 3.0 4.0 ... 28.0 29.0 30.0 31.0
  * sector        (sector) float64 0.0 30.0 60.0 90.0 ... 270.0 300.0 330.0
    sector_ceil   (sector) float64 15.0 45.0 75.0 105.0 ... 285.0 315.0 345.0
    sector_floor  (sector) float64 345.0 15.0 45.0 75.0 ... 255.0 285.0 315.0
    west_east     (point) float64 5.147e+05
    south_north   (point) float64 4.621e+06
Dimensions without coordinates: point
Data variables:
    wdfreq        (sector, point) float64 0.05314 0.03321 ... 0.1148 0.0707
    wsfreq        (wsbin, sector, point) float64 0.02601 0.04219 ... 0.0 0.0
Attributes:
    Conventions:      CF-1.8
    history:          2024-02-16T10:48:50:\twindkit==0.7.1.dev17+ga2ef68d\tcr...
    wasp_header:      SerraSantaluzia
    Package name:     windkit
    Package version:  0.7.1.dev17+ga2ef68d
    Creation date:    2024-02-16T10:48:50
    Object type:      Binned Wind Climate
    author:           DTU CI Config
    author_email:     pywasp@dtu.dk
    institution:      DTU Wind

Read in topography data

Secondly, let’s also read in some elevation and landcover data and combine it into a pywasp.wasp.TopographyMap

In [8]: elev_map = wk.read_vector_map(path / "SerraSantaLuzia.map", crs="EPSG:32629", map_type="elevation")

In [9]: lc_map, lc_tbl = wk.read_vector_map(path / "SerraSantaLuzia.map", crs="EPSG:32629", map_type="roughness")

In [10]: topo_map = pw.wasp.TopographyMap(elev_map, lc_map, lc_tbl)

Calculate Generalized Wind climate

A observed wind climate can be generalized with WAsP, using the pywasp.wasp.generalize function

In [11]: gwc = pw.wasp.generalize(bwc, topo_map)

In [12]: print(gwc)
<xarray.Dataset>
Dimensions:        (point: 1, sector: 12, gen_roughness: 5, gen_height: 5)
Coordinates:
    height         (point) float64 25.3
    south_north    (point) float64 4.621e+06
    west_east      (point) float64 5.147e+05
    crs            int8 0
  * sector         (sector) float64 0.0 30.0 60.0 90.0 ... 270.0 300.0 330.0
    sector_ceil    (sector) float64 15.0 45.0 75.0 105.0 ... 285.0 315.0 345.0
    sector_floor   (sector) float64 345.0 15.0 45.0 75.0 ... 255.0 285.0 315.0
  * gen_roughness  (gen_roughness) float64 0.0 0.03 0.1 0.4 1.5
  * gen_height     (gen_height) int64 10 25 50 100 250
Dimensions without coordinates: point
Data variables:
    A              (sector, gen_height, gen_roughness, point) float32 5.69 .....
    k              (sector, gen_height, gen_roughness, point) float32 1.717 ....
    wdfreq         (sector, gen_height, gen_roughness, point) float32 0.06491...
Attributes:
    Conventions:      CF-1.8
    history:          2024-02-16T10:48:50:\twindkit==0.7.1.dev17+ga2ef68d\tcr...
    wasp_header:      SerraSantaluzia
    Package name:     windkit
    Package version:  0.7.1.dev17+ga2ef68d
    Creation date:    2024-02-16T10:48:50
    Object type:      Binned Wind Climate
    author:           DTU CI Config
    author_email:     pywasp@dtu.dk
    institution:      DTU Wind
    title:            Generalized wind climate

Downscale to wind turbine locations

The generalized wind climate can be used with the topography data to predict the wind climate at nearby locations, like the hub-height of planned wind turbines

In [13]: import pandas as pd

In [14]: wtg_locs = pd.read_csv(path / 'turbine_positions.csv')

In [15]: output_locs =  wk.create_dataset(wtg_locs.Easting.values,
   ....:                                  wtg_locs.Northing.values,
   ....:                                  wtg_locs['Hub height'].values,
   ....:                                  crs="EPSG:32629")
   ....: 

In [16]: pwc = pw.wasp.downscale(gwc, topo_map, output_locs=output_locs, genwc_interp="nearest")

In [17]: print(pwc)
<xarray.Dataset>
Dimensions:        (sector: 12, point: 15)
Coordinates:
  * sector         (sector) float64 0.0 30.0 60.0 90.0 ... 270.0 300.0 330.0
    height         (point) int64 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50
    south_north    (point) int64 4622313 4622199 4622336 ... 4624252 4624142
    west_east      (point) int64 513914 514161 514425 ... 515808 516060 516295
    crs            int8 0
    sector_ceil    (sector) float64 15.0 45.0 75.0 105.0 ... 285.0 315.0 345.0
    sector_floor   (sector) float64 345.0 15.0 45.0 75.0 ... 255.0 285.0 315.0
Dimensions without coordinates: point
Data variables:
    A              (sector, point) float32 7.428 7.399 7.054 ... 7.52 7.75 8.305
    k              (sector, point) float32 2.029 2.037 2.041 ... 2.127 2.123
    wdfreq         (sector, point) float32 0.06445 0.06485 ... 0.07584 0.0792
    site_elev      (point) float32 459.7 460.0 460.0 427.1 ... 520.0 520.0 520.0
    air_density    (point) float32 1.163 1.163 1.163 1.167 ... 1.156 1.156 1.156
    wspd           (point) float32 7.434 7.257 6.978 7.08 ... 7.548 7.818 8.102
    power_density  (point) float32 414.6 385.3 340.9 368.9 ... 426.1 481.9 532.1
Attributes:
    Conventions:      CF-1.8
    history:          2024-02-16T10:48:53:\twindkit==0.7.1.dev17+ga2ef68d\t w...
    title:            WAsP site effects
    Package name:     windkit
    Package version:  0.7.1.dev17+ga2ef68d
    Creation date:    2024-02-16T10:48:54
    Object type:      Met fields
    author:           DTU CI Config
    author_email:     pywasp@dtu.dk
    institution:      DTU Wind

Estimate the gross AEP

The predicted wind climates can be used to estimate the Annual Energy Production in GWh for a specific turbine model

In [18]: wtg = wk.read_wtg(path / "Bonus_1_MW.wtg")

In [19]: gross_aep = pw.wasp.gross_aep(pwc, wtg)

In [20]: print(gross_aep)
<xarray.Dataset>
Dimensions:      (point: 15)
Coordinates:
    height       (point) int64 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50
    south_north  (point) int64 4622313 4622199 4622336 ... 4624252 4624142
    west_east    (point) int64 513914 514161 514425 ... 515808 516060 516295
    crs          int8 0
Dimensions without coordinates: point
Data variables:
    gross_AEP    (point) float32 2.905 2.767 2.552 2.629 ... 2.996 3.189 3.407
Attributes:
    Conventions:      CF-1.8
    history:          2024-02-16T10:48:53:\twindkit==0.7.1.dev17+ga2ef68d\t w...
    title:            WAsP site effects
    Package name:     windkit
    Package version:  0.7.1.dev17+ga2ef68d
    Creation date:    2024-02-16T10:48:55
    Object type:      Anual Energy Production
    author:           DTU CI Config
    author_email:     pywasp@dtu.dk
    institution:      DTU Wind

In the User Guide we go into more much greater detail with each component in PyWAsP.