How Sunny is Seattle?
A comparative look at solar resource availability
Is solar energy a viable resource for a city like Seattle?
My friend recently asked me this question.
I believe it's a fair question to ask. Seattle isn't known for its abundance of sunshine. As I write this post, it is a gloomy, cloudy and rainy day in Seattle. For those familiar with Seattle weather, this is not a surprise - fall and winter months are often cloudy and rainy.
In this post, we will answer this question by taking a look at the available solar resource for Seattle. We will also compare it against other cities - Boston, Austin and Berlin.
Note - this post is not a discussion on the differences in the local policies but only the resource availability.
Where do we get solar resource data?
National Renewable Energy Lab (NREL) provides access to the National Solar Radiation Database (NSRDB). NSRDB is collection of hourly and half-hourly values of meteorological data and solar radiation measurements.
We will download the data and perform the analysis using Python (You can also use the web interface to download the data, if you prefer).
To download the data programmatically, we will need a NREL Developer API key - it is free and the signup only requires your name and email (where you will receive the API key).
Installation
We will use a Python API that I have created, nrel-dev-api, to programmatically access data and analysis services from NREL. It currently covers all of the solar API endpoints that NREL provides, with future support for other services such as wind, electricity, etc.
python -m pip install --upgrade nrel-dev-api
Set your API key
from nrel_dev_api import set_nrel_api_key
set_nrel_api_key("YOUR_NREL_API_KEY")
Download NSRDB data
To download the data, we need the latitude and longitude for our city of interest as well as the year and time interval of our data. Seattle's latitude and longitude are 47.61 and -122.35, respectively.
First, we will check to see if there are any data available for the specified year, interval and location.
from nrel_dev_api.solar import (download_nsrdb_data,
get_nsrdb_download_links)
seattle_links = get_nsrdb_download_links(year=2016, interval=60, lat=47.61, lon=-122.35)
seattle_links
['https://developer.nrel.gov/api/nsrdb/v2/solar/psm3-download.csv?names=2016&wkt=POINT%28-122.35+47.61%29&interval=60&api_key=yourapikey&email=youremail']
The above get_nsrdb_download_links returns a list of available links for the location and year of interest. Once we have the links, we can pass them to download_nsrdb_data to download the data.
seattle_hourly_df = download_nsrdb_data(seattle_links[0], email="YOUR_EMAIL")
The download_nsrdb_data function returns a pandas dataframe containing hourly irradiance, temperature, humidity, pressure, etc. The specific value of interest to us here is the column named "GHI" which stands for 'Global Horizontal Irradiance'.
Global horizontal irradiance is the amount of irradiance falling on a surface that is horizontal to the surface of the earth. This value is of particular interest for solar installations.
We will first resample the "GHI" data monthly and then sum it up to create monthly global horizontal irrandiance data.
seattle_ghi_monthly_sum = 1e-3 * seattle_hourly_df["GHI"].resample("M").sum()
Note - the "GHI" data is in units of Wh/m2 and we are converting it to KWh/m2.
The resulting dataframe contains the monthly sum of "GHI" for the year 2016 in Seattle.
2016-01-31 33.661
2016-02-29 49.141
2016-03-31 90.370
2016-04-30 154.304
2016-05-31 169.188
2016-06-30 198.978
2016-07-31 191.013
2016-08-31 180.028
2016-09-30 126.233
2016-10-31 61.287
2016-11-30 35.059
2016-12-31 45.311
Freq: M, Name: GHI, dtype: float64
We can now repeat the process for Boston and Austin to get the monthly sum of "GHI" in 2016, boston_ghi_monthly_sum and austin_ghi_monthly_sum (If you want to see the code for Boston and Austin, check out the jupyter notebook).
Let's put all the monthly GHI data into a single dataframe.
import pandas as pd
import numpy as np
index = ["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"]
ghi_df = pd.DataFrame(
np.array([
seattle_ghi_monthly_sum.values,
boston_ghi_monthly_sum.values,
austin_ghi_monthyl_sum.values
]).T,
columns=["Seattle", "Boston", "Austin"],
index=index
)
Finally, let's get GHI data for Berlin. NSRDB doesn't have data for Berlin but we can get it from PVGIS, a tool provided by the European Union science hub. After downloading the data, we can load it as a pandas dataframe.
berlin_monthly_df = (pd.read_csv("Monthlydata_52.516_13.377_SA_2016_2016.csv", sep="\t", skiprows=4, skipfooter=4)
.dropna(axis=1)
.rename(columns={"H(h)_m" : "monthly_ghi_kWh_m2"})
)
ghi_df["Berlin"] = berlin_monthly_df["monthly_ghi_kWh_m2"].values
Seattle Boston Austin Berlin
Jan 33.661 64.608 111.509 16.03
Feb 49.141 83.216 135.596 37.93
Mar 90.370 119.618 153.963 64.28
Apr 154.304 160.144 162.074 120.44
May 169.188 174.102 166.599 182.66
Jun 198.978 213.426 211.531 173.66
Jul 191.013 199.640 229.077 151.20
Aug 180.028 184.762 187.451 142.08
Sep 126.233 124.293 165.196 117.23
Oct 61.287 96.820 152.188 40.18
Nov 35.059 61.626 93.345 25.73
Dec 45.311 54.708 71.597 15.45
Let's visualize the differences in the irradiance data.
import seaborn as sns
ax = sns.barplot(data=ghi_df, ci="sd")
ax.set_ylabel("KWh/m$^{2}$ per month")
ax.set_title("Global Horizontal Irradiance")
The barplot shows the average global horizontal irradiance per month (with errorbars representing the standard deviation) for the different cities in our dataframe.
ax = ghi_df.plot(
kind="line",
ylabel="KWh per m$^{2}$",
title="Total Monthly Global Horizontal Irradiance",
style=["o-", "o-", "o-", "o-"]
)
The lineplot shows the monthly sum of global horizontal irradiance for the different cities in our dataframe.
# Annual Global Horizontal Irradiance
ghi_df.sum() # KWh/m2
Seattle 1334.573
Boston 1536.963
Austin 1840.126
Berlin 1086.870
dtype: float64
Seattle gets sunlight comparable to Boston but lower than Austin, monthly as well as annually. The fall and winter months receive less sunlight, which is not surprising.
However, when we compare Seattle with Berlin, we see that Seattle gets higher solar irradiance thorughout the year. This is very promising for solar in Seattle (and Washington).
In 2020, Germany produced 51 TWh of electricity from solar despite getting less sun than Seattle! That's 10.5% of Germany's electricity generation.
Of course, there are differences in policies and cost of solar; but looking at the solar resource availability, there is no reason why solar energy can't be more successful in Seattle, Washington and the US in general.
Thanks for reading! You can find the notebook with the code here.