The Power and the Money

Renewables are not causing blackouts in Texas

Texas and northern Mexico are in the middle of an astonishing cold snap. Conditions are so bad that the bad weather pops out on a national map of traffic conditions:

The cold snap is also prompting blackouts across Texas and northern Mexico. And, of course, that means that politicians in both places are blaming renewables for the failure. In Texas, exhibit A for the prosecution consists of this figure:

Three things happen once the cold snap kicks in. First, wind generation falls up as turbines freeze. Second, natural gas generation spikes up. Finally, coal generation becomes steady as plants fire up to full capacity and stay there to cope with spiking demand. As a result, many observers are concluding that Texas would be just fine if only it hadn’t reoriented its energy mix around windpower.

That would be a correct conclusion if not for the fact that it’s all wrong.

Let’s take it in turn.

First, more coal would not have helped. Coal is lousy in cold weather. Back in 2014 — it seems so long ago, I know — six coal plants consisting of 1.8 megawatts of generation went down in a cold snap. (Here is the ERCOT after-action report for the ’14 cold snap.) The same thing happened during a freeze in February 2011, when ERCOT (the Texan grid operator) had to impose rolling blackouts. In 2014 (and in 2021) natural gas took up the slack, but in 2011 that didn’t work when power cuts hit compression stations.

Second, on paper Texas has more than enough generating capacity right now to handle demand. The state has 75 GW of natural gas capacity alone, to which you can add another 24 GW of coal. The problem is that the capacity is either down for maintenance, in the wrong place, or offline due to the cold, not that it doesn’t exist. In fact, you can see that Texas handled similar levels of demand this summer. The difference is that people expect summer heat waves in Texas. They don’t expect Minnesota-like weather to suddenly roll in from nowhere.

Finally, let’s put points (1) and (2) together and point out that right now the big problem in Texas is due to fossil fuel plants going offline from to the cold. Wells and pipelines are freezing. How much Texas thermal capacity is out, you ask? Well, I’ll tell you:

Thirty gigawatts.

Thirty gigawatts!

Getting the data isn’t easy, but it isn’t that hard either. Go here. Click on “Hourly Resource Outage Capacity.” You will see a long list of zipped CSV files. Open the top one. Look for the column called “TotalResourceMW.” (It’s the first one after the time.) That tells you how much power is out right now. If you read down in the spreadsheet you’ll see how quickly ERCOT expects that power to come on line. (This includes coal, natural gas, oil, and nuclear; as a practical matter, no nuclear is off-line in Texas.) “TotalIRRResourcesMW” is the amount of renewable power capacity that is not producing (for any reason) and “TotalNewEquipResourceMW” are resources that are being spun up but have not yet started to inject power into the grid. As of 4pm CST on February 15^th, 2021, 29.7 GW of thermal power is offline.

Now, it isn’t fair to say that all 29.7 GW are down because of the cold. Some power is always offline. I called up February 8^th, before temperatures started to drop. And as of 11am on February 8^th, some 13.1 GW of thermal was offline. Which means, as a first estimate, the cold knocked out about 17 gigawatts of thermal capacity, roughly ten nuclear reactors.

To be frank, I’m surprised it isn’t more. Coal operators in Texas right now are performing miracles. You’ve got to use anti-freeze on the coal supplies to stop them from icing up (particularly lignite coal, which is 35% water) and keep the fan blades on your cooling towers ice-free. You’ve also got to tightly pack your coal piles to keep water from seeping in and be prepared to smash ‘em with bulldozers in case that fails. (The only coal plants I’ve visited up-close-and-personal were in Colombia and they didn’t do any of these things, obviously.) The fact that coal generation is holding steady is quite the feat.

But the fact is that Texas is hitting problems because it ain’t ready for the cold. Renewables aren’t the problem. The state has a total capacity of 25.1 GW of wind and 3.8 GW of utility solar, so right now the ERCOT data that I cited above means that about 6.5 GW of renewables are hitting the grid. That is abovethe 6.1 GW that ERCOT expects in the winter.  

What can be done? Well, the simple answer is better planning. The more complicated one is better incentives. Texas should set up a capacity market that will give natural gas operators financial or contractual reasons to keep their plants on-line during cold weather. Ditto insuring that wind farms are prepared for the cold. A few more nuclear reactors (which would relegate more natural gas to peakload) wouldn’t hurt either, but isn’t going to happen.

Total decarbonization will not be easy until storage becomes extremely cheap. You will either need peak plants or massive battery banks to cope with the Great Blizzard of 2054. And the former will mean somehow maintaining the natural gas infrastructure despite an order-of-magnitude less demand. But these are all solvable problems which would exist even if we went hog-crazy for expensive and polluting coal plants.

TLDR: Texas would be in trouble even if it had never built a wind farm.  So think about that and stop pretending that you don’t care about your unborn grandchildren.

The 2020 Horserace (regressions)

Joint with Leticia Abad

The 2020 election was strange, and not just because the President denied he had lost, was believed by millions, and inspired a violent plot to use a mob to seize control of Congress and overturn the election.

No, it would have been strange even without all that. Georgia and Arizona went blue. Florida went red. Donald Trump picked up nonwhite votes — open bigotry notwithstanding — and upscale suburbanites voted for what may be the most left-wing Democratic platform since 1976. (Or maybe 1948.)

So what happened? Well, we have been gathering county-level electoral, Covid, and BLM-related data for another project. Numbers in hand, we decided to take a look at the 2020 election.

The first thing we did was to look at some simple correlations. What county-level characteristics were related to a bigger swing towards Donald Trump between 2016 and 2020?

Figure 1 Simple correlations with the 2020 swing towards the GOP, by county

The above figure shows our results. Negative numbers represent swings towards the Democratic candidate. Dark shading means that the correlation is tight and significant at the 95 percent level; moderate shading means it’s significant at 90%, and very light shading means that it is not significant. The ethnic markers refer to self-identified ethnicity and not foreign-origin; in other words, in all cases they represent mostly citizens (save for Central Americans).

These results, of course, don’t mean much. Several of those variables are highly correlated with each other. For example, did Mexican-American counties swing towards Trump because they are Mexican-American or because Mexican-Americans are relatively young? Moreover, they don’t all for geographic variation. Did Puerto Ricans move away from Trump because they are Puerto Rican or because lots of them live in New York and New Jersey? So all the above numbers should be taken with a grain of salt, both the ones that jibe with the conventional wisdom and the ones which don’t.

To start the long process of teasing out which variables mattered, we ran a simple OLS “horserace” regression. And here is where things get interesting!

Figure 2

Let’s take it from the top. The above figure shows the coefficient (i.e., the correlation controlling for other things) for various variables. It also shows the error bars. If the error bar crosses the zero line, then there is a good chance that the effect you see is really the product of random chance. Positive numbers mean a swing towards President Trump; negative numbers mean a swing towards Joe Biden relative to Hillary Clinton in 2016.

Conceptually, these regressions compare different counties within a state. That is, swings in one way or another that uniformly affect every county and voting group within a state are already accounted for. (In technobabble, we use state fixed effects and cluster standard errors at the state level.) These numbers capture the average swing for a group across the country relative to the statewide swing where they live. 

So what do we get? First, a pair of mysteries! One, Covid deaths do not seem to have had much effect. Two, BLM protests in a county are associated with a small (but statistically significant) swing towards the Democrats … and if anything, the swing was greater if the protests were violent. Admittedly, this result is neither surprising nor free from endogeneity.* But it is different from what Omar Wasow found when he analyzed the effect of the 1960s-era protests and riots.

The Latino swing towards the GOP shows up fairly clearly. The higher the share of Mexican-Americans, Cubans, or Dominicans in a county, the bigger the swing towards the GOP. Asian-Americans show the same pattern. On the other hand, Blacks and Puerto Ricans swung fairly clearly towards the Democrats: reports from the exit polls of Trumpian inroads among African-Americans do not appear to be borne out in these data.**

Finally, an unexpected result, which you can’t quite see in this data. A lot of ink has been spilled on the rural-urban divide in America. And it’s real! But it’s not really cultural. From our data, large central metro counties swung about three percentage points more Democratic in 2020 than the rest of their state, ceteris paribus. But everyone else — exurban counties, small metro areas, “micropolitan” counties, and rural places — was about the same. That is to say, when you account for demographic differences, there isn’t much of a rural/urban split in this country. There’s just a small giant-center-city/everyone-else split.

But there’s a problem with these results! The regressions treat every county equally. Alpine County, California, (population 1,129) is treated the same as Los Angeles (population 9,969,510). It could very well be that small shifts in tiny counties are driving our results. So we weighted our regressions by population, and report both results below.

Figure 3

So what changes? First, our coefficients on covid deaths, the share of college graduates, and BLM protests doesn’t change. Neither does the African-American swing towards the Democrats.

Second, the effect of unemployment goes away. That was driven by small counties. That effect disappears when you give the big cities their proper weight. (Yes, that is a little surprising.)

Third, the peculiarities of Mexican-Americans, Puerto Ricans, and Dominican-Americans also go away. It turns out that the Mexican and Dominican swings towards the GOP were driven by small tiny counties. (We identified a few counties with precisely one Dominican resident.) Same thing with the Puerto Rican swing towards the Democrats. When you give the populous counties their due weight, the effects all disappear. Only the Cuban swing holds up as a real thing that happened.

This isn’t to say that it’s a bad idea for Democrats to try to appeal to the Mexican-American voters in the Rio Grande Valley who broke hard for the Republican incumbent. It’s never a bad idea to try to appeal to any constituency! But they seem to have been a peculiar group and we’re not sure that the putative Latino swing to the GOP is really worth bothering about, since we’re not sure that it happened. (Cubans, that’s a different story. They really did swing towards Trump, but the reasons are not that mysterious. Even you did not expect the GOP’s demonization of socialism to work as well as it did — and we did not — it should not come a massive surprise that it did work.)

Finally, it does indeed look as though Asian-Americans in the big cities and suburbs swung back towards the GOP. We can try to break that out by ethnicity, and we will! But that’s the real swing in our data, the one that nobody seems to be talking much about, maybe because it’s more fun to blame things on Mexicans.

To end with a plea: any thoughts on research designs designed to tease out the reasons behind the counterintuitive results on Covid deaths and BLM-related unrest?

* Yes, yes, “endogeneity” is technobabble. Sue us.

** A problem with Central Americans is that most are not citizens. If you include a variable for the share of noncitizens in a county, then the coefficient on Central Americans disappears. Since non-citizens do not vote, it is possible then that the effect is actually that citizens in counties with lots of non-citizens tend to be related to those aforementioned non-citizens, and people with non-citizen relatives swung towards the Democrats. But because the share of non-citizens and the share of Central Americans tend to be correlated, it’s hard to disentangle what’s going on.