10-01-2014 Update: Yes, a conclusive update is in the works. After another protracted, failed job search and some pointless interviews, I now have time to look anew at the EPM data and create a standard deviation among absolute differences in retail electricity price.
Of course, this means retrieving my spreadsheet from late June and updating it to show how many standard deviations a statewide increase in retail electricity price is, relative to the national mean price for that period.
With that, back to the graphing paper I go…
I gathered the courage to call Mr. Hoopman and ask what he thinks about my report. Contrary to the hang-up I thought was coming, Dave kindly spoke with me and explained he saw absolute differences to be greater in restructured states, referring vaguely to the EIA in general — as I had mentioned the agency — but not to EPM specifically.
I told him that I’m interested in his perspective and would return to my spreadsheet to calculate absolute change rather than percentage only. That episode was a reminder of how 3 credits of research design class at a state university really doesn’t drill in the need to compare both absolute and relative differences.
Then, we could get into confidence intervals and margins of error, which the EIA already estimates from its surveys of power plants. But I’ll update again when I have the chance, due to spending most of my time trying to find a professional job (and commenting on the job search milieu in the process).
Today’s report was inspired by Dave Hoopman’s article, “You can’t go back: Better infrastructure won’t make restructuring a winner,” in the March 2014 issue of Wisconsin Energy Cooperative News.
He included a map of which states were actively restructuring, suspended restructuring, or did not restructure their respective energy markets. In the penultimate paragraph, Hoopman wrote, “…While electric rates have risen in every state since the ‘90s, they’ve risen more in the states that restructured than in the ones that didn’t.”
That is quite the blanket statement, but Dave did not cite data to support his claim. I thereby saw an opportunity to challenge the experts: Was Hoopman ignoring the facts? Why would I bother to find out? Because I value truth over falsity — and because I love a challenge!
Finding the Data
I used Internet searches to find retail electricity rates by year, so as to compute percentage change between historical and current. I found the website for accessing current and prior issues of Electric Power Monthly (EPM), a monthly compilation of plant-by-plant and aggregate data summarized by the Energy Information Administration (EIA).
Because power plants are required by law to complete and submit five forms to the EIA and one to the Federal Regulatory Energy Commission (FERC) — the latter of which is also inspected by the EIA prior to publication — EPM data is the most comprehensive and accurate one may find.
The farthest back for which EPM archives are available is the June 1996 issue, which contains March 1996 data. I considered using only end-of-year data for expediency but ultimately decided to research both January-to-January and July-to-July retail electricity prices to see whether the difference in end-of-year rate changes between restructured and nonrestructured markets holds true for operating conditions in the opposite weather.
To compare season-to-season rates among the retail, commercial, industrial, transportation, and overall sector then and now, I copied data for the mid-summer period 18 years ago; last mid-summer; the late winter period 17 years ago; and last late winter for each sector. My spreadsheet therefore captures differences at vastly different times of the year to detect whether the time of year changes the season-to-season price gap between market structures to any noticeable extent.
As shown in Table ELO, the band of 29 nonrestructured intrastate energy markets experienced an additional one-fifth (factor of 1.2 in both industrial transportation sectors) and one-third (factor of 1.33 in both residential and commercial sectors) rate hike over the group of 22 entities (21 states and the District of Columbia) that had restructured their respective energy markets.
Table ELO (so named for SEO purposes)
|Nonrestruct. Avg. Jan’97-Jan’2014||58.26%||53.74%||62.77%||15.48%||60.10%|
|Restruct. Avg. Jul’96-Jul’2013||39.59%||34.90%||49.66%||12.61%||45.22%|
I modified Hoopman’s “market restructuring” map to overlay the percentage price change between January 1997 and January 2014. This is but another representation of the tabular data found in my spreadsheet, which more fully demonstrates the inverse relationship between energy market restructuring and retail electricity price in each sector.
Although one third of states identified as restructured had suspended partway through the process, I dichotomized those into the category “restructured” because they changed from the “non-restructured” cooperative power plant model. The similarity of rates indicates the annotation of “suspended” or “active” is a trivial distinction when it comes to the bottom line of retail electricity prices, irrespective of sector.
The substantive differentiator is how local cooperatives have actually increased prices on end users more than power plants in restructured markets. From this, we may conclude: Hoopman is wrong! Or more tactfully stated, he misspoke when claiming restructured markets were any cheaper for the end user in any sector. Let the casual and industry reader alike understand: Restructuring saves money to the end users of each sector — namely, between one-fifth and one-third of retail electricity price!
I have therefore shown historical data that reveal the refusal to restructure its energy market, while perhaps in the self-interest of that practically control the energy market, has not saved Wisconsin consumers any money through “lower rate increases” alleged by the cooperatives and their lobbyists, when compared to states that did restructure respective energy markets. To explain how would require a study of system dynamics, but for now my research has opened the gates for further investigation.
Ideas for Further Study
Cheaper electricity is good if reliability is not substantially impaired. By “substantially,” I’ll say more than 5 percent more downtime among outages. And because even the shortest of power outages may cause costly data loss and require slow-to-boot machines to regain functionality, I’ll go even further and say the discrete number of outages per generator should not exceed 3 per year.
Although the mass media tends to emphasize outages from heavy use of air conditioners in summer, the EPM data reveal greater winter-to-winter rate increases than what occurred summer-to-summer. The need for outage data is necessary to correlate outage frequency with rate increase, but I did not have access to relevant data for that purpose. If Hoopman or his colleagues have such data, then I look forward to a future article on the matter.
When identified, such data will be useful to further evaluate the general claim of restructuring as more positive than negative for consumers. That ambition is beyond the scope of this study because the purpose of this report was to examine Hoopman’s claim, which extended only to retail electricity prices but not to outages.
A Call for Correction
Dave may have carelessly overlooked the back issues of EPM or — more sinisterly — perhaps did his homework but nonetheless ignored EPM data. We may, however, infer several facts:
1) As a staff writer for a magazine about power generation cooperatives, Hoopman is a lobbyist on their behalf and therefore faces the moral hazard of ignoring data contrary to the cooperatives’ freedom.
2) Even with a monthly deadline hanging overhead, Dave could have either offered to write a different article for that issue — while he gets data to support his statement in a later issue — or to have omitted that baseless statement altogether.
In other words, there’s no excuse for a seasoned energy market writer to have made any such statement that grossly contradicts official data! Whether Wisconsin Energy Cooperative News publishes a response remains to be seen, but I will update this article if any of its staff do comment.
I’m not out to make anyone miserable, but I am out to promote honesty in how we arrive at public policy decisions. If that means taking a lobbyist organization to task one volunteer blogger against a multimillion-dollar staff of dozens, then so be it. I already face down those odds while advocating for student-worker unemployment compensation!
Defying the majority when they’re plain wrong — being an independent thinker who questions those hired by their friends and entrapped by their colleagues’ group thought — is part of why I’ve not also been shepherded into professional employment as the experts have been.
Yet, I prove them wrong and less competent through my own resources, even if they’ll always have a good-paying job and I won’t. The satisfaction in a job well done is often all one can get.
If you’re not offended by my martyr complex, then plunk a few dollars into my PayPal account by clicking the button below and following the payment wizard!
July 1996 prices are taken from Table 52 on page 75 of 165 from the October 1996 EPM.
January 1997 prices are taken from Table 53 on page 76 of 166 from the April 1997 EPM.
July 2013 prices are taken from Table 5.6.A on page 118 of 188 from the September 2013 EPM.
January 2014 prices are taken from Table 5.6.A on page 123 of 217 from the March 2014 EPM.