Categories
updates

New PUDL Software & Data Release: v0.4.0

In August we put out a new PUDL software and data release for the first time in 18 months. We had a lot of client work, and kept putting off doing the release, so a whole lot of changes accumulated. Some highlights, mostly based on the continuously updated release notes in our documentation:

New Data Coverage

  • EIA Form 860 added coverage for 2004-2008, as well as 2019.
  • EIA Form 860m has been integrated (through Nov 2020). Note that it only adds up-to-date information about generators (especially their operational status).
  • EIA Form 923 added the 2001-2008 data, as well as 2019.
  • EPA CEMS Hourly Emissions covering 2019-2020.
  • FERC Form 714 covering 2006-2019, but only the table of hourly electricity demand by planning area. This data is still in beta and the data hasn’t been integrated into the core SQLite database, but you can process it on the fly if you want to work with it in Pandas.
  • EIA Form 861 for 2001-2019. Similar to the FERC Form 714, this ETL runs on the fly and the outputs aren’t integrated into the database yet, but it’s available for experimental use.
  • US Census Demographic Profile 1 (DP1) for 2010. This is a separate SQLite database, generated from a US Census Geodatabase, which includes census tract, county, and state level demographic information, as well as spatial boundaries of those jurisdictions.
Categories
linkstream weeknotes

SQL for data analysis, DGP, and pair programming

Some good technical long reads from the last couple of weeks:

(Postgre)SQL for Data Analysis

Before the Tidyverse and Pandas, there was SQL. There’s still SQL, and as Vicki Boykis often points out: every data-centric framework that hangs around long enough tends toward SQL. It’s got almost half a century of careful thinking and optimization behind it. It seems entirely possible that it’ll still be around after another half century.

In this extensive post Haki Benita explores a bunch of data analysis that can be done directly with PostgreSQL in particular. It can be used either as an efficient preprocessing step before handing off to other tools, or to generate final products. It covers basic data selection, random selection, sampling, splitting data into training & testing sets, descriptive statistics, aggregations, regressions, interpolation, binning and much more. It’s almost more of a pocket guide to data analysis in SQL than a blog post.

Data (Error) Generation Processes

In this post Emily Riederer explores how conceptualizing data (and error!) generation processes can help you do better data validation. What does the data represent in the real world? How is it being collected? How does it move from where it’s collected to where it’s processed? What kinds of transformations operate on it before you look at the outputs? Understanding these steps and their contexts makes it easier to imagine how things can go wrong along the way and what errors to check for. It also makes it easier to debug errors when you find them.

On Pair Programming

A guide to pair programming from Birgitta Böckeler and Nina Siessegger. They look at both how and why to do it, and some of the challenges that it brings up. I had no idea that this has been a practice going back as far as the women who programmed ENIAC.

The authors explore several different styles of pair programming and the logistical planning required to make it work. They touch on the extra challenges of doing remote pairing which seems extra relevant these days. They cover productive and destructive social dynamics that come up, and a whole lot more. The article is long, but it’s definitely worth a read if you’ve thought about trying pair programming and been reluctant, or have tried it and been dissatisfied.

Categories
weeknotes

What we’re reading for the week of March 1st, 2021

A roundup of interesting posts related to data, code, energy, or climate that we came across in the first week of March, 2021.

Energy & Climate

  • Xcel Energy’s Comanche 3 coal plant in Colorado continues to be an expensive boodoggle. It’s spent 2 of its first 10 years of operation shut down for maintenance, forcing Xcel to buy electricity on the open market to fill the gap. But actually this was good for their customers, since the electricity the plant produces when it is running is so expensive ($66.25/MWh) that this actually saved customers money! Unfortunately they’ll still be on the hook for remaining capital costs far into the future. Xcel thinks they’ll use the plant as a seasonal or load following resource after 2030… at which point Xcel will still have $460M left in the plant.
  • Market Design for the Clean Energy Transition: Advancing Long-Term Approaches. Materials from a workshop put on by WRI and Resources for the Future, exploring how electricity markets need to adapt to accommodate lots of zero carbon, very low marginal cost generation, that’s also not entirely dispatchable.
  • A post from the Energy and Policy Institute looking at political “miscellaneous” spending by utilities, as reported in the FERC Form 1 — using our data!
  • Securitization in Action: How US States are Shaping an Equitable Coal Transition: a post from some of our collaborators at RMI, looking at some of the work our data liberation has helped enable — namely getting uneconomic fossil generation offline as cheaply as possible. Well, as cheaply as possible without forcing the utilities to absorb the costs anyway.
  • CMIP6: the next generation of climate models explained. A look at how climate scientists compare their models in a standardized way, so that they can understand why they get different answers sometimes. This is something we really need more of in the energy modeling space — otherwise every conversation eventually devolves into criticizing the inputs and assumptions.

Data & Code

  • Command Line Interface Guidelines: a collection of best practices for designing modern command line tools that are relatively user friendly, and take advantage of many features of modern Unix terminals.
  • Column Names as Contracts: an interesting post by Emily Reiderer about the potential benefits of storing metadata in column names using a controlled vocabulary, allowing them to be programmatically parsed.
  • Embedding column-name contracts in data pipelines with dbt builds on that last post, and looks at how Jinja templates and tools like dbt let you do more interesting dynamic data work if your columns have consistent and controlled names.
  • What is dbt anyway? It stands for “data build tool” and it can be used to specify, store, and version control complex data transformation instructions as text files. A lot of the data we’re working with from FERC and EIA are too messy for this to be helpful in our initial ETL process, but once we’ve got the databases being populated in the cloud automatically, this could be a good way to create new derived data products. Thanks to our friend Brittany Bennett at Sunrise Movement for telling us about dbt.
  • I helped build ByteDance’s censorship machine. A story about what it’s like to work inside a tech company actively implementing censorship measures. ByteDance is the Chinese owner of TikTok.
  • Documentation for pydantic. We’re trying to make all of our metadata programmatically accessible, and remove duplication wherever possible, and using pydantic to parse and validate the metadata we compile by hand so we know it’s at least structurally sound.
  • Python Packages is an online book about how to package and distribute… Python packages. We wish we’d had this a couple of years ago when we were figuring it out for the first time! Focuses on modern rather than legacy frameworks, going straight for pyproject.toml, poetry, and CI/CD using GitHub actions. There’s also a cookie cutter repo on GitHub that templates many of the practices from the book. Via Tiffany Timbers.
  • EPA has released a crosswalk table that connects their CEMS data to the EIA boilers and generators. Thank goodness we won’t have to compile it now. More info in their GitHub repo.
  • Nice preprint from Ryan Abernathey et al. on cloud-native scientific data repositories. This is very much in line with our plans for the PUDL data — even though our data is several orders of magnitude smaller than a lot of what he’s talking about.
  • Eliminating Toil is a short essay from some Googlers on the nature of a particular kind of work that shows up in many data wrangling (and software) contexts. A lot of our mission here is saving others from data toil.
  • Great Expectations and Pandas Profiling: a blog post on how to use these two tools together to automatically draft data validation test cases. Vaguely along the same lines as Pandera, though that library has more of a statistical bent.
Categories
updates

PUDL Infrastructure Roadmap for 2021

A couple of weeks ago I attended TWEEDS 2020 virtually (like everything this year) and talked about Catalyst’s ongoing Public Utility Data Liberation (PUDL) project, and especially the challenges of getting a big pile of data into the hands of different kinds of users, using different tools for different purposes. It ended up sketching out a bit of a PUDL infrastructure roadmap for the next year, and so we thought it would be a good idea to write it up here too.

We’ll have a separate post looking at our 2021 data roadmap.

The US Energy Information Asymmetry

PUDL is all about addressing a big information asymmetry in the regulatory and legislative processes that affect the US energy system. Utilities have much more information about their own systems than policymakers and advocates typically do. As a result, regulators often defer to the utilities on technical & analytical points. Commercial data exists, but it’s expensive. We want to get enough data into the hands of other kinds of stakeholders that they can make credible quantitative arguments to regulators, and challenge unfounded assertions put forward by utilities.

Federal Agencies and Their Favorite File Formats
Categories
analysis

Heat Rate Calculation for EIA Generators

Catalyst is pulling together an estimate of the marginal cost of electricity (MCOE) for every natural gas and coal fired power plant in the US whose data we can get our hands on. We’re using data from the EIA 923, EIA 860, and FERC Form 1 to do it.  Getting the heat rate right for each generator is an important part of this calculation, but a lot of the required data is… not perfect. Here’s how we’re working through it.