May global surface TempLS down 0.028°C from April.

The TempLS FEM anomaly (1961-90 base) was 0.829°C in May, down from 0.857°C in April. It was still the second warmest May in the record. The NCEP/NCAR reanalysis base index rose by 0.014°C. Both changes were very small

Almost all the world was warm, with a cold spot in India, north into central Asia, and also somewhat in Australia. Most of Canada was very warm. The Pacific W of Peru is starting to warm.

Here is the temperature map, using now the FEM-based map of anomalies.

As always, the 3D globe map gives better detail. There are more graphs and a station map in the ongoing report which is updated daily.

GISS April global temperature down by 0.2°C from March.

The GISS V4 land/ocean temperature anomaly was 1.00°C in April 2023, down from 1.23°C in March. This fall is a little more than the 0.158°C fall reported for TempLS.

As usual here, I will compare the GISS and earlier TempLS plots below the jump.

April global surface TempLS down 0.158°C from March.

The TempLS FEM anomaly (1961-90 base) was 0.859°C in April, down from 1.017°C in March. It was still the fourth warmest April in the record, and the second warmest month since October 2021. The NCEP/NCAR reanalysis base index fell even more by 0.332°C.

Almost all the world was warm, with cold spots just in Alaska, central Siberia down to India, and some of mid west USA. Nowhere was very warm, except a patch in Antarctica.

Here is the temperature map, using now the FEM-based map of anomalies.

As always, the 3D globe map gives better detail. There are more graphs and a station map in the ongoing report which is updated daily.

Hourly modelling of conversion of USA48 to wind/solar, with costing and optimisation - Updated.

Update note - I made an error which inflated the build costs of S&W by 8.76. That greatly inflated the resulting optima, by about that amount. I have rewritten this post; you can see the original here.

Update 2 (two hours later). I realised that I used the mean GW to cost, rather than the built faceplate GW. I have fixed that; optima are approximately doubled.

Update 3 (four hours later). I realised that The first estimate wasn't bad, because I had multiplied the cost by 3 for the capacity factor. Working with faceplate installed, that isn't appropriate. So it's back in the range of 5-6 $T.

Update 4. Not a mistake this time, but an improvement. I've costed wind and solar separately, with wind the same at $1.647/W, but solar is cheaper at $1/Watt. This is preparatory to improve by changing proportion of solar, as per next post. The difference here is small , saving maybe $0.5T.

In a post earlier this month, I looked at a currently circulating claim, most recently from CFACT, that replacement of fossil fuel (FF) generation with wind and solar (W&S) would require impossible amounts of storage. Costs of hundreds of trillions of dollars were mentioned. That earlier posts has links.

The original analysis of hourly IEA data was done by Ken Gregory, of Friends of Science. I pointed out the problem, which was that they had allowed too little generation capacity, so the hypothetical grid was relying on storage to cope with annual fluctuations in demand. No system, FF or otherwise, can reasonably do this. You must have enough generation to cover the annual peak periods.

So I adapted Ken's spreadsheet to allow variable amounts of generation. Bringing it up to adequate levels hugely reduces the storage requirement. Building more quickly makes it very small.

Now increasing the generation also has costs, but nothing like the huge costs of Gregory and CFACT. A small number of trillions. The US grid is big business.

A commenter at WUWT, Old Cocky, suggested that costing would be interesting. Being the sum of a rising cost of build with rapidly falling storage costs, there must be an optimum. Ken's model was that W&S would increase proportionally (factor H) to meet the demand formerly met by FF, while nuclear and hydro would remain unchanged (and so not in the analysis). You can analyse hourly data for just one year to get the minimum storage that would get through the year.

Data collection

I have recently been able to extend the data to the four years 2019-2022, which seem to be all the full years IEA has. It would be tempting to use a multi-year set, but the problem is that wind has been growing rapidly, so if the expansion of 2019 is sufficient, then later years are wastefully more than sufficient. I could have tested one year of wind on four years of variable demand, but that misses the point, which is looking for the effect of rare bad wind episodes.

Costs

There are basically just two costs that matter for this optimisation, and I've used Ken's numbers, for which he gives the source. The capital cost of building W&S is set at $1647 per KW. And for storage, $347 per KWh. To cost the build, we need the currently installed base (to be multiplied by H). I had to look that up in Wikipedia; the values (for all USA, W&S) for 2019-2022 are 0.1821 0.2190 0.2563 0.2813 TW.

Optimising

A small change from the last post (and KG). H is now the ratio of new (not old+new) W&S to old W&S. So values are one less than in that post.

As shown in that post, the storage requirement and cost reduces rapidly (exponentially) with H. But the build cost increases linearly with H. So somewhere there is an optimum. Here is a table of computed values around the optimum, for the years 2019-2022. The first column is H, the second is minimum storage needed, computed as in the earlier post. The third is the build cost in $B, formed by multiplying the faceplate GW by the cost per GW. The fourth is storage (col 2) multiplied by storage cost/TWh, and the fifth is the sum, for which we want the minimum (bolded).

For any H, as years progress the build cost increases and the storage cost decreases. This is because the base W&S level is increasing. So the optimum moves to lower H. Y

2019	Storage TWh	Cost(H*S&W) $T	Storage $T	Sum Costs $T
12	26.551	3.005	2.521	5.526
13	18.724	3.256	1.778	5.033
14	15.606	3.506	1.482	4.988
15	13.226	3.756	1.256	5.012
16	10.935	4.007	1.038	5.045
2020	0	0	0	0
10	26.514	2.983	2.372	5.354
11	15.274	3.281	1.366	4.647
12	9.506	3.579	0.85	4.429
13	7.163	3.877	0.641	4.518
14	6.091	4.176	0.545	4.72
2021	0	0	0	0
10	33.56	3.442	3.13	6.571
11	17.073	3.786	1.592	5.378
12	9.635	4.13	0.899	5.029
13	7.142	4.474	0.666	5.14
14	6.153	4.818	0.574	5.392
2022	0	0	0	0
9	18.396	3.354	1.751	5.105
10	8.13	3.727	0.774	4.501
11	3.302	4.1	0.314	4.414
12	1.967	4.473	0.187	4.66
13	1.117	4.845	0.106	4.952

Discussion

The amounts are much less that the hundreds of trillions in the CFACT report, and now around $5T (US GDP is about $22T). How should we think about this? Well, electricity generation is big business, and this represents renewal over, say, 27 years. That would have cost a lot in any technology, and of course produces a system with much reduced fuel costs. But here are some factors which might increase or reduce it

• The obvious big thing that might increase it is the need to be more conservative about storage. The criterion for each year was to get through that year with storage not drained. There will be worse years.
• The obvious big thing that might decrease it is continued reduction in costs, which have been coming down a lot.
• Another big reduction comes from the artificial requirement that the dips in W&S that drain storage are proportional to H. That would be true if the original sites were each expanded by that factor. But new sites will be found, and their diversity will smooth out the dips in W&S.
• A very big reduction comes if cheaper forms of storage than battery are used, as they will be, particularly pumped storage.
• In Europe at least, a big reduction will come from improved interconnection, allowing trade in surpluses, and also rationalisation in location. It is much better to spend on solar in Spain (or Morocco) than in Germany. And in Mexico rather than Maine.
• The model assumed hydro would continue as before. But there will be a continued market-led shift of hydro to drain dams only when W&S are low (and prices high). This has the effect of a big storage increase.

Incidentally, storage costs at the optima are less than build costs.

Conclusion

As before, the very high levels of storage in the reports by Ken Gregory and CFACT are ridiculous. You have to optimise, and then the costs come down to manageable levels (about $5T) as capital cost to replace FF with W&S and battery storage. And there are many ways of ameliorating them.

Postscript

I have added the csv files for each of the years 2019-2022 to the earlier zipfile.

I thought about ways to test all years in a continuous optimisation. The problem is that W&S has been increasing rapidly, so how should it be expanded. If uniformly, the storage troubles of 2019 will dominate. There isn't any point in using one W&S year expanded for 2019-22; the point is to test the variability of W&S over four years.

I tried detrending W&S (and FF), so 2019 was in effect pre-expanded. But this was still not good enough; 2019 was still the limiting year, because exaggerated expansion leads to exaggerated variability. So I'm stuck, for the moment, with doing individual years.

GISS March global temperature up by 0.23°C from February.

The GISS V4 land/ocean temperature anomaly was 1.21°C in March 2023, up from 0.98°C in February. This rise is the same as the 0.229°C rise reported for TempLS.

As usual here, I will compare the GISS and earlier TempLS plots below the jump.

March global surface TempLS up 0.229°C from February.

The TempLS FEM anomaly (1961-90 base) was 1.008°C in March, up from 0.779°C in February. It was the second warmest March in the record, after 2016. The NCEP/NCAR reanalysis base index rose by 0.235°C.

Almost all of Eurasia was warm, with a very warm band from Ukraine to the Pacific. There was a cool to cold region in W US and Canada, but warm in the East. The cold ENSO plume in the E Pacific has gone.

Here is the temperature map, using now the FEM-based map of anomalies.

As always, the 3D globe map gives better detail. There are more graphs and a station map in the ongoing report which is updated daily.

CFACT says Net Zero is impossible? Debunked.

I've been arguing again at WUWT. This time is is an article by David Wojick, of CFACT, titled A Simple Reason Why Net Zero Is Impossible. It has an associated report, which in turn is based on a report by Ken Gregory, of Friends of Science. This also has an ancestor, but that is probably far enough back. Wojick describes Gregory's report as "breakthru", so between them they will probably get a lot of circulation.

 
The basic claim is that if you replaced the total USA48 power generated in 2019 by a scaled up version of the 10% of that which was wind and solar (W&S)), then you would need 250 TWh of storage to make it work, and this is impossible.

 
It is of course wrong. To explain qualitatively, an issue with both fossil fuel (FF) generation and W&S is that demand varies throughout the year. It is possible to generate just the average required, and use storage to meet the peak. But traditionally, this is of course never done. Enough generation is always provided to meet the peak, so that some is idled at other times. And you would do the same with W&S, with a subtle difference that there is no need to idle outside the peak, since no fuel is required.

 
What these reports do is to wrongly underestimate the amount of W&S required, so it is not meeting the peak, and so is using storage to cover the annual variation. That accounts for the huge storage estimates. The reason is that they scale up W&S so the average matches the FF average, although I think even then they underscale. But it is the wrong thing to do, because the FF profile was sculpted to match the peaks, by idling at other times. The 10% W&S profile did not have that requirement, and so if you scale it by average, it won't match the peaks.

Quantitative scaling

Ken Gregory has an extensive spreadsheet here which has the basic data I used. It has hourly generation figures of each source for 2019, and also other years, but I'll stick to 2019. Ken calculated a "target" T, which was total generation excluding nuclear and hydro. I'm not sure why the exclusion, but I'll do that too (it isn't much). Then he looked at the difference between this T, and W&S multiplied by a scale factor. I will call the factor H, and the product HW. He used values about H=7. He accumulated the difference HW-T, which became the storage S. He showed that if S has to be positive through the year, then it could rise as high as 250 TWh.

 
I use a slightly different, and more realistic approach to storage. I specify a maximum storage Sm. Then at each hourly step, the difference HW-T (which might be negative) is added to S only to the extent that Sm is not exceeded. In fact, I set Sm=0, because you can add a constant without changing anything. Then the storage required is the minimum (negative) value reached during the year. Of course, this is the bare minimum for just getting through 2019; a reserve will have to be added to H to allow for less favorable years.

 
So I did that using various factors H. In fact H=10 is what would scale up W&S to match total demand for 2019. I got the following values for minimum storage required:

H Min storage needed TWh 7.3 241 10 51 12 8 15 2.4 20 0.44 25 0.11

It is close to exponential decrease. And in my reckoning, H=15 is closest to matching the existing FF build, and 2.4 TWh is not an impossible amount of storage. But building a bit more W&S reduces this a lot further.
Here is a graph of the various cumulative storages. The x-axis is in hours of the year 2019. You can see that at H=7.3 the storage does have to make up for a big change in annual demand, while at H=10, is is only needed to cover the short term changes, since there is enough generation to cover the peak.



I'll show the same on a log scale, same colors. It distorts the annual cycle, but gives a better picture of the higher H storage behaviour.

Conclusion

Ken Gregory, amplified by David Wojick, claim that a simulation of 2019 electricity generation for USA48 with wind and solar only shows a requirement for very large storage (250 TWh). But as siimilarly simulated here, that is because they provided too little W&S, thus requiring storage to cover the annual demand cycle. Doubling the provision reduces storage to 2.5 TWh, with further exponential decrease.
The R code and data used are in a zipfile here

Update - I've added an XL file and a readme.txt to the zipfile.