Progress Wind Module

class progress.mod_wind.Wind

Bases: object

Handles downloading, assembling, and analyzing wind data from the NREL Wind Toolkit for resource adequacy or other power system studies.

This class provides methods to:

  1. DownloadWindData: Fetch and parse wind speed data (at specified heights) from NREL’s Wind Toolkit for each wind site/year.

  2. WindFarmsData: Read and organize farm-level metadata (capacity, turbine rating, etc.) along with power curve information.

  3. CalWindTrRates: Compute transition rate matrices for wind speed classes based on time series data, useful in Markov chain-based reliability or adequacy models.

CalWindTrRates(directory, windspeed_data, site_data, pcurve_data)

Computes and stores transition rate matrices for wind speed classes using Markov chain assumptions. Each site’s wind speed time series is discretized into classes, and a transition probability matrix is computed.

Process Summary:

  1. Reads wind speed data from the merged CSV created by DownloadWindData().

  2. Bins wind speed observations into classes based on the start speeds defined in the power curve CSV.

  3. Increments counts for transitions between consecutive hours (e.g., from class j to class k).

  4. Converts transition counts to probabilities and writes each site’s transition matrix to an Excel file.

Parameters:

  • directory (str) – Directory to save the resulting transition rate Excel file.

  • windspeed_data (str) – Path to the CSV file containing wind speed time series for all sites (output of DownloadWindData()).

  • site_data (str) – Path to the CSV containing wind farm data (Farm No., Farm Name, etc.).

  • pcurve_data (str) – Path to the CSV containing power curve data (defining wind speed classes).

Returns:

A 3D NumPy array of shape (sites, classes, classes) representing transition probability matrices for each site. Each [i, j, k] entry is the probability of moving from class j to class k at site i.

Return type:

np.ndarray

Raises:

  • FileNotFoundError – If any of the CSV files (windspeed_data, site_data, pcurve_data) are missing.

  • pd.errors.EmptyDataError – If any CSV is empty or cannot be parsed.

DownloadWindData(directory, site_data, api_key, email, affiliation, year_start, year_end)

Downloads wind speed data from the NREL Wind Toolkit for the given sites and years, then consolidates it into a single CSV file.

Process Summary:

  1. Reads site metadata (longitude, latitude, hub height) from the specified CSV.

  2. Iterates through each site and year to request wind speed data at 80m and 100m heights from the NREL API.

  3. Applies power-law interpolation if a site’s hub height differs from 80m and 100m.

  4. Merges all sites (for each year) into a single DataFrame, adjusting timestamps, and finally writes a combined CSV of wind speeds at each site.

Parameters:

  • directory (str) – Directory where downloaded data and final merged CSV will be saved.

  • site_data (str) – Path to the CSV file containing site metadata (Farm Name, Longitude, Latitude, Hub Height, etc.).

  • api_key (str) – Your personal API key for accessing the NREL Wind Toolkit.

  • email (str) – Your email address (required by the NREL API).

  • affiliation (str) – Your organization or affiliation (required by the NREL API).

  • year_start (int) – The first (earliest) year of data to download.

  • year_end (int) – The last (latest) year of data to download.

Returns:

None

Return type:

None

Raises:

  • FileNotFoundError – If the site CSV file is missing or unreachable.

  • requests.exceptions.RequestException – For network or API-related failures during data download.

WindFarmsData(site_data, pcurve_data)

Reads and organizes wind farm metadata and power curve data to facilitate wind power output calculations in subsequent modeling.

Process Summary:

  1. Reads the wind farm CSV to extract site/farm identifiers, zone numbers, and capacities (MW).

  2. Computes the number of turbines per farm based on rated capacity and turbine rating.

  3. Reads the power curve CSV to identify wind speed class thresholds and power output curves for Classes 2 and 3.

Parameters:

  • site_data (str) – Path to the CSV containing wind farm data (Farm No., Farm Name, Zone No., Max Cap, Turbine Rating, etc.).

  • pcurve_data (str) – Path to the CSV containing power curve data (Start/End speeds, Class 2 curve, Class 3 curve, etc.).

Returns:

A 10-element tuple containing:

  • w_sites (int): Number of wind farm sites.

  • farm_name (pd.Series): Names of each wind farm.

  • zone_no (np.ndarray): Zone indices corresponding to each wind farm.

  • w_classes (int): Number of discrete wind speed classes.

  • w_turbines (np.ndarray): Number of turbines at each site.

  • turbine_rating (np.ndarray): Rated power (MW) of each turbine.

  • p_class (np.ndarray): Array indicating which power curve class (2 or 3) each site uses.

  • out_curve2 (np.ndarray): Power output curve for Class 2 turbines (indexed by wind speed class).

  • out_curve3 (np.ndarray): Power output curve for Class 3 turbines (indexed by wind speed class).

  • start_speed (np.ndarray): Lower bounds of wind speed classes (m/s).

Return type:

tuple

Raises:

  • FileNotFoundError – If either CSV (site_data or pcurve_data) is not found.

  • pd.errors.EmptyDataError – If either CSV file is empty or cannot be parsed.