on the topic of average retail energy price

this is what happens when i have time on my hands. its a cautionary tale perhaps but the results are interesting and provides a gateway for even further research analysis. i’m a fan of energy data sites — all of them. i’ve used them to win bar bets, end arguments (and start a few), augment deliverables and, when they were younger, lull my children into a long and blissful sleep. but i digress.

ISO-NE Avg Retail Prices

my latest dive was purely based on curiosity; i wanted to see how residential retail electricity prices moved for customers in states that participate in an ISO/RTO. the government EIA (http://www.eia.gov/) is particularly good at delivering datasets for this type of analysis. the EIA, however, doesn’t adjust for inflation so i also needed a set of conversion factors; it just so happens a shiny new set was published in January by the fine folks of the political science department at oregon state university (http://oregonstate.edu/cla/polisci/sahr/sahr).

linked below is the result of my morning’s effort: a spreadsheet with an interactive pivot table where you can select an ISO/RTO and/or an individual state to see the average retail price (cents per kilowatthour) of electricity between the years of 1990 and 2012 — normalized to 2012 dollars.

the spreadsheet is macro-free, code-free, free-free, and provides links to all the source data i used to construct. it contains 5 tabs:

  1. pivot — this is a basic pivot table with filters enabled, you can select and deselect how you want to view the data. it also comes with a dynamic graph that repaints each time you change something.
  2. average price per state columnar data table — this is my table created from the source data and the pivot works off of this.
  3. ISO.RTO — this is a list of all 50 states plus DC and 8 other territories that i created to populate the pivot.
  4. source price data — self explanatory.
  5. source conversion factors data — ditto.
noteworthy:
  • ISO/RTOs do not abide by state boundaries and this analysis doesn’t distinguish between states that participate in whole (i.e PJM: Pennsylvania) and those who are partially involved (i.e. PJM: North Carolina)
  • for the purpose of this analysis, the district of columbia is often considered a state but the territories are not
  • 37 states, or 73%, (see previous bullet) participate (whole or in part) with an ISO/RTO
  • between 1990 and 2012 california saw a reduction (from one year to the next) in average retail price more times than an increase occurred. the largest reduction was 0.54 cents, which occurred twice, and the largest increase was 1.34 cents.
  • overall, retail prices for states participating actually appears to drop between these years. i didn’t expect this.

 

averages and datasets such as these often paint a fuzzy picture and there are a plethora of factors influencing price. this exercise was only intended to crack open the topic — a litmus test if you will. feel free to download the spreadsheet, play with it, modify it, whatever you like. standard disclaimers apply but if you enhance it with other cross-reference datasets, or find my logic flawed, please post your comments for open discussion.

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