diff --git a/prereise/gather/demanddata/eia/clean_data.py b/prereise/gather/demanddata/eia/clean_data.py
index c72dc4207..866ab1141 100644
--- a/prereise/gather/demanddata/eia/clean_data.py
+++ b/prereise/gather/demanddata/eia/clean_data.py
@@ -4,7 +4,7 @@
 
 def fix_dataframe_outliers(demand):
     """Make a dataframe of demand with outliers replaced with values
-        interpolated from the non-outlier edge points using slope_interpolate
+    interpolated from the non-outlier edge points using slope_interpolate
 
     :param pandas.Dataframe demand: demand data frame with UTC time index
         and BA name as column name
@@ -21,10 +21,10 @@ def fix_dataframe_outliers(demand):
 
 def slope_interpolate(ba_df):
     """Look for demand outliers by applying a z-score threshold to the demand
-        slope. Loop through all the outliers detected, determine the non-outlier
-        edge points and then interpolate a line joining these 2 edge points. The
-        line value at the timestamp of the the outlier event is used to replace
-        the anomalous value.
+    slope. Loop through all the outliers detected, determine the non-outlier
+    edge points and then interpolate a line joining these 2 edge points. The
+    line value at the timestamp of the the outlier event is used to replace
+    the anomalous value.
 
     :param pandas.DataFrame ba_df: demand data frame with UTC time as index and
         BA name as column name
@@ -111,7 +111,7 @@ def slope_interpolate(ba_df):
 
 def replace_with_shifted_demand(demand, start, end):
     """Replaces missing data within overall demand dataframe with averages
-        of nearby shifted demand
+    of nearby shifted demand
 
     :param pandas.DataFrame demand: Dataframe with hourly demand where the
         columns are BA regions
diff --git a/prereise/gather/demanddata/eia/get_eia_data.py b/prereise/gather/demanddata/eia/get_eia_data.py
index 095132194..485bd7bba 100644
--- a/prereise/gather/demanddata/eia/get_eia_data.py
+++ b/prereise/gather/demanddata/eia/get_eia_data.py
@@ -8,8 +8,8 @@
 
 
 def from_download(tok, start_date, end_date, offset_days, series_list):
-    """Downloads and assemble dataset of demand data per balancing authority \
-        for desired date range.
+    """Downloads and assemble dataset of demand data per balancing authority
+    for desired date range.
 
     :param str tok: token obtained by registering with EIA.
     :param datetime.datetime start_date: start date.
@@ -39,7 +39,7 @@ def from_download(tok, start_date, end_date, offset_days, series_list):
 
 def from_excel(directory, series_list, start_date, end_date):
     """Assembles EIA balancing authority (BA) data from pre-downloaded Excel
-        spreadsheets. The spreadsheets contain data from July 2015 to present.
+    spreadsheets. The spreadsheets contain data from July 2015 to present.
 
     :param str directory: location of Excel files.
     :param list series_list: list of BA initials, e.g., ['PSE',BPAT','CISO'].
@@ -69,7 +69,7 @@ def from_excel(directory, series_list, start_date, end_date):
 
 def get_ba_demand(ba_code_list, start_date, end_date, api_key):
     """Downloads the demand between the start and end dates for a list of
-        balancing authorities
+    balancing authorities
 
     :param pandas.DataFrame ba_code_list: List of BAs to download from eia
     :param datetime.datetime start_date: beginning bound for the demand df
@@ -87,7 +87,7 @@ def get_ba_demand(ba_code_list, start_date, end_date, api_key):
 
 class EIAgov(object):
     """Copied from `this link <https://quantcorner.wordpress.com/\
-        2014/11/18/downloading-eias-data-with-python/>`_.
+    2014/11/18/downloading-eias-data-with-python/>`_.
 
     :param str token: EIA token.
     :param list series: id code(s) of the series to be downloaded.
diff --git a/prereise/gather/demanddata/eia/map_ba.py b/prereise/gather/demanddata/eia/map_ba.py
index df152e5a5..003a37f54 100644
--- a/prereise/gather/demanddata/eia/map_ba.py
+++ b/prereise/gather/demanddata/eia/map_ba.py
@@ -4,7 +4,7 @@
 
 def transform_ba_to_region(demand, mapping):
     """Transforms column of demand dataframe to regions defined by
-        dictionary mapping
+    dictionary mapping
 
     :param demand: dataframe for the demand
     :type demand: pandas.DataFrame
@@ -35,7 +35,7 @@ def transform_ba_to_region(demand, mapping):
 
 def map_to_loadzone(agg_demand, bus_map):
     """Transforms columns of demand dataframe from BA regions to load zones
-        according to bus_map
+    according to bus_map
 
     :param agg_demand: dataframe for the aggregated region demand
     :type agg_demand: pandas.DataFrame
@@ -71,7 +71,7 @@ def map_to_loadzone(agg_demand, bus_map):
 
 def map_grid_buses_to_county(grid):
     """Find the county in the U.S. territory that each load bus
-        in the query grid belongs to
+    in the query grid belongs to
 
     :param grid: the name of the query grid
     :type grid: Grid
diff --git a/prereise/gather/hydrodata/eia/get_profile_by_us_monthly_factor.py b/prereise/gather/hydrodata/eia/get_profile_by_us_monthly_factor.py
index c39a10488..a1f59d643 100644
--- a/prereise/gather/hydrodata/eia/get_profile_by_us_monthly_factor.py
+++ b/prereise/gather/hydrodata/eia/get_profile_by_us_monthly_factor.py
@@ -5,7 +5,7 @@
 
 def get_profile(hydro_plant, start="2016-01-01-00", end="2016-12-31-23"):
     """Creates hydro profile from monthly capacity factors reported by EIA
-        `here <https://www.eia.gov/electricity/annual/html/epa_04_08_b.html>`_.
+    `here <https://www.eia.gov/electricity/annual/html/epa_04_08_b.html>`_.
 
     :param pandas.DataFrame hydro_plant: data frame with *'Pmax'* as
         column and *'plant_id'* as indices.
diff --git a/prereise/gather/solardata/ga_wind/ga_wind.py b/prereise/gather/solardata/ga_wind/ga_wind.py
index 50180fe3b..4462d7235 100644
--- a/prereise/gather/solardata/ga_wind/ga_wind.py
+++ b/prereise/gather/solardata/ga_wind/ga_wind.py
@@ -14,7 +14,7 @@ def retrieve_data(
     solar_plant, hs_api_key, start_date="2007-01-01", end_date="2014-01-01"
 ):
     """Retrieves irradiance data from Gridded Atmospheric Wind Integration
-        National dataset.
+    National dataset.
 
     :param pandas.DataFrame solar_plant: data frame with *'lat'*, *'lon'* and
         *'Pmax'* as columns and *'plant_id'* as indices.
diff --git a/prereise/gather/solardata/helpers.py b/prereise/gather/solardata/helpers.py
index 867f02619..3cff57ae9 100644
--- a/prereise/gather/solardata/helpers.py
+++ b/prereise/gather/solardata/helpers.py
@@ -34,7 +34,7 @@ def to_reise(data):
 
 def get_plant_info_unique_location(plant):
     """Identify unique location and return relevant information of plants at
-        location.
+    location.
 
     :param pandas.DataFrame plant: plant data frame.
     :return: (*dict*) -- keys are coordinates of location. Values is a list of
diff --git a/prereise/gather/solardata/nsrdb/naive.py b/prereise/gather/solardata/nsrdb/naive.py
index e7a1356fc..5607619b1 100644
--- a/prereise/gather/solardata/nsrdb/naive.py
+++ b/prereise/gather/solardata/nsrdb/naive.py
@@ -7,7 +7,7 @@
 
 def retrieve_data(solar_plant, email, api_key, year="2016"):
     """Retrieve irradiance data from NSRDB and calculate the power output
-        using a simple normalization.
+    using a simple normalization.
 
     :param pandas.DataFrame solar_plant: data frame with *'lat'*, *'lon'* and
         *'Pmax' as columns and *'plant_id'* as index.
@@ -22,35 +22,24 @@ def retrieve_data(solar_plant, email, api_key, year="2016"):
     # Identify unique location
     coord = get_plant_info_unique_location(solar_plant)
 
-    # Build query
-    attributes = "ghi"
-    leap_day = "true"
-    interval = "60"
-    utc = "true"
-
-    # URL
-    url = "http://developer.nrel.gov/api/solar/nsrdb_psm3_download.csv?"
-    url = url + "api_key={key}".format(key=api_key)
-
-    payload = (
-        "names={year}".format(year=year)
-        + "&"
-        + "leap_day={leap}".format(leap=leap_day)
-        + "&"
-        + "interval={interval}".format(interval=interval)
-        + "&"
-        + "utc={utc}".format(utc=utc)
-        + "&"
-        + "email={email}".format(email=email)
-        + "&"
-        + "attributes={attr}".format(attr=attributes)
-    )
+    base_url = "https://developer.nrel.gov/api/solar/nsrdb_psm3_download.csv"
+    payload = {
+        "api_key": api_key,
+        "names": year,
+        "leap_day": "true",
+        "interval": "60",
+        "utc": "true",
+        "email": email,
+        "attributes": "ghi",
+    }
+    qs = "&".join([f"{key}={value}" for key, value in payload.items()])
+    url = f"{base_url}?{qs}"
 
     data = pd.DataFrame({"Pout": [], "plant_id": [], "ts": [], "ts_id": []})
 
     for key in tqdm(coord.keys(), total=len(coord)):
         query = "wkt=POINT({lon}%20{lat})".format(lon=key[0], lat=key[1])
-        data_loc = pd.read_csv(url + "&" + payload + "&" + query, skiprows=2)
+        data_loc = pd.read_csv(f"{url}&{query}", skiprows=2)
         ghi = data_loc.GHI.values
         data_loc = pd.DataFrame({"Pout": ghi})
         data_loc["Pout"] /= max(ghi)
diff --git a/prereise/gather/solardata/nsrdb/sam.py b/prereise/gather/solardata/nsrdb/sam.py
index be0a01b70..936cb6534 100644
--- a/prereise/gather/solardata/nsrdb/sam.py
+++ b/prereise/gather/solardata/nsrdb/sam.py
@@ -3,7 +3,7 @@
 import numpy as np
 import pandas as pd
 from powersimdata.network.usa_tamu.constants.zones import (
-    id2state,
+    id2abv,
     interconnect2state,
     state2interconnect,
 )
@@ -19,7 +19,7 @@
 
 def retrieve_data(solar_plant, email, api_key, ssc_lib, year="2016"):
     """Retrieves irradiance data from NSRDB and calculate the power output using
-        the System Adviser Model (SAM).
+    the System Adviser Model (SAM).
 
     :param pandas.DataFrame solar_plant: data frame with *'lat'*, *'lon'* and
         *'Pmax' as columns and *'plant_id'* as index.
@@ -47,28 +47,18 @@ def retrieve_data(solar_plant, email, api_key, ssc_lib, year="2016"):
     # Identify unique location
     coord = get_plant_info_unique_location(solar_plant)
 
-    # Build query
-    attributes = "dhi,dni,wind_speed,air_temperature"
-    interval = "60"
-    utc = "true"
-
-    # URL
-    url = "http://developer.nrel.gov/api/solar/nsrdb_psm3_download.csv?"
-    url = url + "api_key={key}".format(key=api_key)
-
-    payload = (
-        "names={year}".format(year=year)
-        + "&"
-        + "leap_day={leap}".format(leap="false")
-        + "&"
-        + "interval={interval}".format(interval=interval)
-        + "&"
-        + "utc={utc}".format(utc=utc)
-        + "&"
-        + "email={email}".format(email=email)
-        + "&"
-        + "attributes={attr}".format(attr=attributes)
-    )
+    base_url = "https://developer.nrel.gov/api/solar/nsrdb_psm3_download.csv"
+    payload = {
+        "api_key": api_key,
+        "names": year,
+        "leap_day": "false",
+        "interval": "60",
+        "utc": "true",
+        "email": email,
+        "attributes": "dhi,dni,wind_speed,air_temperature",
+    }
+    qs = "&".join([f"{key}={value}" for key, value in payload.items()])
+    url = f"{base_url}?{qs}"
 
     data = pd.DataFrame({"Pout": [], "plant_id": [], "ts": [], "ts_id": []})
 
@@ -79,7 +69,7 @@ def retrieve_data(solar_plant, email, api_key, ssc_lib, year="2016"):
     zone_id = solar_plant.zone_id.unique()
     frac = {}
     for i in zone_id:
-        state = id2state[i]
+        state = id2abv[i]
         frac[i] = get_pv_tracking_ratio_state(pv_info, [state])
         if frac[i] is None:
             frac[i] = get_pv_tracking_ratio_state(
@@ -91,11 +81,12 @@ def retrieve_data(solar_plant, email, api_key, ssc_lib, year="2016"):
 
     for key in tqdm(coord.keys(), total=len(coord)):
         query = "wkt=POINT({lon}%20{lat})".format(lon=key[0], lat=key[1])
+        current_url = f"{url}&{query}"
 
-        info = pd.read_csv(url + "&" + payload + "&" + query, nrows=1)
+        info = pd.read_csv(current_url, nrows=1)
         tz, elevation = info["Local Time Zone"], info["Elevation"]
 
-        data_resource = pd.read_csv(url + "&" + payload + "&" + query, skiprows=2)
+        data_resource = pd.read_csv(current_url, skiprows=2)
         data_resource.set_index(
             dates + timedelta(hours=int(tz.values[0])), inplace=True
         )
diff --git a/prereise/gather/solardata/pv_tracking.py b/prereise/gather/solardata/pv_tracking.py
index bcdc927c3..f25254ea2 100644
--- a/prereise/gather/solardata/pv_tracking.py
+++ b/prereise/gather/solardata/pv_tracking.py
@@ -1,7 +1,7 @@
 import os
 
 import pandas as pd
-from powersimdata.network.usa_tamu.constants.zones import id2state
+from powersimdata.network.usa_tamu.constants.zones import abv
 
 
 def get_pv_tracking_data():
@@ -32,8 +32,7 @@ def get_pv_tracking_data():
 
 
 def get_pv_tracking_ratio_state(pv_info, state):
-    """Get solar PV tracking technology ratios for the query state in 2016 from
-        EIA860
+    """Get solar PV tracking technology ratios for the query state in 2016 from EIA860
 
     :param pandas.DataFrame pv_info: solar pv plant information as found in
         form EIA860 as returned by :func:`get_pv_tracking_data`.
@@ -47,7 +46,7 @@ def get_pv_tracking_ratio_state(pv_info, state):
         raise TypeError("state must be a list")
 
     for s in state:
-        if s not in set(id2state.values()):
+        if s not in abv:
             raise ValueError("Invalid State: %s" % s)
 
     pv_info_state = pv_info[pv_info["State"].isin(state)].copy()
diff --git a/setup.py b/setup.py
index 05a9f60ae..5b9b7dea3 100644
--- a/setup.py
+++ b/setup.py
@@ -4,9 +4,9 @@
     name="prereise",
     version="0.3",
     description="Create and run an energy scenario",
-    url="https://github.com/intvenlab/PreREISE",
+    url="https://github.com/Breakthrough-Energy/PreREISE",
     author="Kaspar Mueller",
-    author_email="kmueller@intven.com",
+    author_email="kaspar.mueller@breakthroughenergy.org",
     packages=find_packages(),
     package_data={
         "prereise": [
@@ -15,6 +15,7 @@
             "gather/data/EIA923_Schedules_2_3_4_5_M_12_2016_Final_Revision.xlsx",
             "gather/hydrodata/data/hydro_gmt.csv",
             "gather/hydrodata/data/texas_hydro_gmt.csv",
+            "gather/solardata/data/3_3_Solar_Y2016.csv",
         ]
     },
     zip_safe=False,