Month: April 2017

US COURT TO DECIDE ON CLIMATE REGULATION ROLL BACK

US COURT TO DECIDE ON CLIMATE REGULATION ROLL BACK

via climate science
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This piece explains. It seems the president is having to fight in the courts to get most of his policies through. This is the strength of democratic nations which have so many checks and balances. It is also a frustration when voters vote for change and it gets blocked.

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April 10, 2017 at 06:30PM

Properly Representing Wind and Solar in Electric Systems: Generation Capacity (Part I)

Properly Representing Wind and Solar in Electric Systems: Generation Capacity (Part I)

via Master Resource
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“The reality is that non-dispatchable generation technologies, (wind and solar) cannot be directly compared with dispatchable generation technologies (coal, natural gas, nuclear, biomass, and generally speaking, hydro). This is a common mistake.”

Unfortunately, unrealistic representations of wind and solar are alive and well in many publications. Even if mathematically correct, their ability to reflect reality should always be carefully assessed.

These are like maps that show only a few major features, such as coastlines, mountains, large rivers, and major roads, leaving out the likes of non-fordable smaller rivers, marshes, ravines, and steep slopes or cliffs.

Signs to watch out for:

  • Wind and solar are included in graphs comparing capacities or capital investments.
  • Representing that capacities, electricity production, and costs per watt-hour of electricity produced can be used to directly compare non-dispatchable wind and solar with reliable, controllable generation plants.
  • Extensive use of percentages (for growth or share of totals) for comparisons.
  • Reliance on arguments from authority, which assume that if an “authority” says something, it must be true and can be confidently depended upon and repeated without question.
  • Combining primary fuel sources into grouped categories, such as all “renewables,” and combining gas and oil. Oil plays a very small and diminishing role in electricity production. Such combinations can mask important realities.
  • Use of overly dramatic or sensationalistic terms.

In the January 2017 issue of Power magazine, the lead article in the Global Review (titled “IEA: Coal Boom is Over”) provides an opportunity to illustrate this. The title will be taken by the reader as the main message, and it sets the stage for the graphics displayed: “1. A generation shift” that deals with capacities, and “2. Global generation by fuel and demand.” These are the focus of this post series. Part I will address the first, and Part II the second.

The underlying document, an International Energy Agency (IEA) paper (the recent World Energy Outlook) is available only at a considerable price, so this review is based only on what is available in Power magazine itself.

Capacity Comparisons

The first chart, Figure 1 below, is re-created from the figure “A generation shift” in the magazine article. It apparently indicates that renewables (hydro, wind, solar, and biomass) show a trend toward dominating global electricity-generation capacity by 2040, and presumably beyond.

 

 Part I Figure 1

Figure 1 – Re-created capacity projections from Power magazine article.

Note that all renewables (hydro, wind, solar, and biomass) have been grouped together, which conceals important aspects. Combined wind and solar are shown separately in Figure 2 for further analysis.

 

 Pt I Fig 2 (1)

Figure 2 – Power magazine chart with wind and solar shown separately.

 

Necessary Adjustments

The reality is that non-dispatchable generation technologies (wind and solar) cannot be directly compared with dispatchable generation technologies (coal, natural gas[1], nuclear, biomass, and, generally speaking, hydro). This is a common mistake. The DOE/EIA cautions about this in levelized costs for generation technologies[2], and the same considerations apply to other generation characteristics such as capacity and electricity produced. Graphics 1 and 2 in the article suggest otherwise.

Figure 3 shows the effect of reducing wind and solar capacities to that which can be expected over time based on the natural energy flows of wind and solar. This is typically about 25% of total capacity. This production is not controllable by system operators, as is capacity use for hydro (with exceptions such as some run-of-river installations), coal, gas and nuclear plants. So, to start on the path of a proper basis of comparison, wind and solar must be discounted by about 75%.[3] This is shown in Figure 3.

 

Part 1 Fig 3 (1)

Figure 3 – Reducing wind and solar capacities to 25% of maximum due to nature-dictated capacity factor.

On some occasions, and on a short-term basis, wind and solar will generate a much higher percent of their installed capacities. This is often claimed as a major contribution to the grid, whereas in fact it represents a problem to grid management. In many cases, such production will likely be curtailed or dumped to another jurisdiction at a considerable discount.

This is still not sufficient to make proper comparisons, because beyond the above considerations, when called upon by system operators, there may be no electricity production capability from wind and solar at that time.

The relevant measure is ‘capacity credit’, which is a measure of the reliability of contributing to supply at any point in time, and is especially critical (and measured) at peak demand periods. This is important to overall electricity system reliability. More information can be seen in a European Wind Energy Association (EWEA) document.[4]

The capacity credit for non-dispatchable technologies is very low and decreases as their penetration into the electricity system increases. It starts at about the same level as the capacity factor at extremely low penetrations and exponentially falls to below 10 percent and approaches zero as penetration increases. Figure 3 shows the resulting reliable capacity values using 10 percent, which is generous.

The capacity credit for the other technologies approaches 100 percent except for scheduled and unscheduled maintenance.

Part I Fig 4

 

Figure 4 – Reducing wind and solar capacities using a capacity credit of 10%.

Figure 4 now allows comparisons to be made, and wind and solar play very minor roles compared to dispatchable generation technologies. The relative comparisons between technologies will be summarized below in Table 1.

Note the considerable reduction in “real” capacity in 2040 from about 11,000 GW to about 9,000 GW. Another way to look at this is that wind and solar represent a duplication of generation capacity compared to that required to reliably meet demand as shown in the next section.

Duplicate Capacities

How do electricity systems accommodate the nature of wind and solar? They do this by having redundant capacity almost equalling the renewable capacities as shown in Figures 5 and 6 for two jurisdictions that have heavily invested in wind and solar – Germany and Ontario, Canada.

Pt I Fig 5

Figure 5 – Duplicate capacity requirements for Germany in 2015.

Source: See note 4, sub point a.

 

Part 1 Fig 6

Figure 6 – Duplicate capacity requirements for Ontario, Canada, in 2018

Source: Ontario Power Authority[5]

In both figures, the left-hand columns are peak demand requirements and include all the dispatchable capacity that is required to reliably meet demand and provide operating reserve. In the right-hand columns, if you look very carefully, you can see the capacity credit for wind by the slight reduction in “Peak Demand + Op Reserve.” In summary, when wind and solar are added, the other generation plants are not displaced, and, relative to requirements, wind and solar are virtually all duplicate capacity.

Summary of Installed Capacities in 2040

The Power magazine article does not directly show the total installed capacities by fuel type in 2040. Table 1 does this based on the above realistic determinations of the real value of the capacities.

Table 1 – Installed Capacities Adjusted for Capacity Credit

 Part 1 Table 1--Mar 24

All renewables still show an increase as a percentage of the total, but coal and gas each have more capacity installed in 2040 than all renewables (adjusted for capacity credit).  In terms of actual increases in absolute values, the capacity increases for these three are similar.

Note that hydro and biomass (largely hydro) represent over 90% (26/28) of the renewables in 2040, and that wind and solar are 2% of the total capacity.

Although coal and gas are less as a percentage of the total, the absolute levels have increased notably. Part II will look at the actual electricity produced, which is a step towards a better measure for comparisons than installed capacity, especially in illustrating the relative need for the various fuels.

Conclusions

Wind and solar installed capacities are not a good measure of their value to electricity systems. After installed capacities are properly adjusted to represent reality, they play only a very minor role.

Coal capacity still represents a significant growth over 2015, although it is slightly reduced as a percentage. This hardly warrants the dramatic terminology in the title of the Power magazine article, and the chart used in support.

Duplicate capacities of fossil fuel generation plants are necessary when industrial-scale wind and solar are present in electricity systems.

Nuclear is an under-used resource to provide a capability to reduce the use of fossil fuels.

[1] Oil plays an increasingly insignificant role in electricity generation and this category will often be referred to as ‘natural gas’ or simply ‘gas’.

[2] See DOE/EIA document http://ift.tt/2hdErNC. Note comments in the second paragraph on pages 2 and 4, and that wind and solar are shown separately in the following tables.

[3] Other generation technologies are taken to be 100% as the full capacity is available as required by the electricity system operator, except for scheduled and unscheduled maintenance. It is acknowledged that wind and solar Capacity Factors may improve for new builds over time. Solar is the most likely to experience technology improvements. Wind is a very old mechanical conversion process that can be improved slightly with larger and taller wind turbines. Countering any improvements, indications are that wind turbines will degrade notably within the realistic 15-20 year lifetime as explained here.

[4] See http://ift.tt/2p1DYPc, on pages 122–24. This should be read carefully. To transfer the x-axis in Figure 34 to another jurisdiction, it must be converted to wind penetration percentage.

See also:

[5] The Long Term Energy Plan (LTEP) is produced by the Ontario Power Authority, which has just recently been merged with the system operator (IESO). The LTEP is not as easily accessible there, except for a brochure-like summary. The information shown is from the LTEP Cost of Electricity Service 2013 LTEP: Module 4, which I had previously saved.

The post Properly Representing Wind and Solar in Electric Systems: Generation Capacity (Part I) appeared first on Master Resource.

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April 10, 2017 at 06:05PM

South Australia Demolishes their Last Coal Power Station

South Australia Demolishes their Last Coal Power Station

via Watts Up With That?
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Guest essay by Eric Worrall While Federal politicians bicker, South Australia, the world’s renewable crash test dummy, has wasted no time demolishing their last viable coal power station, to lock in their pursuit of an energy free future. Senate inquiry sparks ideological fight over Australia’s energy supply and climate change By political reporter Angelique Donnellan […]

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April 10, 2017 at 04:00PM

Reporting bias and the “increase” in weather events in the US

Reporting bias and the “increase” in weather events in the US

via Watts Up With That?
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Guest essay by Alberto Z. Comendador

In a recent article I discussed the apparent increase in tornadoes in the US since systematic reporting began, in the early 50s.

image

I showed how, if one looked at the year-on-year change in temperatures, there was no correlation with the change in tornado counts. The advantage of using year-on-year changes is that the factors that could lead to an observation or reporting bias are almost completely absent: the population of a state, coverage of Doppler radar, etc. will change very little in that timeframe.

So it appears that the increase is due to improved/expanded reporting, not because there are in fact more tornadoes. This is essentially uncontroversial: NOAA gives a similar explanation on its website, though they get around the observation bias with a different method.

Today I want to look at the other weather events NOAA counts. These are:

  • Hail, since 1955
  • Thunderstorms since 1955, too – on paper. In practice there were almost no events reported until 1995, so that’s what I’ll show here
  • 29 other event categories since 1996. As you can imagine these run the whole gamut, from reasonable to mystifying (‘winter weather’)

There are two ways to look at the change in the number of events. One is what we could call the long-term method: simply drawing a chart like the one above for tornadoes. The other is the short-term method, which is what I did in the previous article: checking if event counts rise when temperatures increase, and if they decline when temperatures fall.

I’m especially interested in the recent events because observation bias is supposed to be stronger the farther back one goes in time. In other words, one should see a very strong bias comparing 2015 with 1955, but perhaps not with 1995. Additionally, in such a short period of time there couldn’t have been a strong warming; the lower-48 US had virtually the same temperature in 1995 as in 2014. It seems reasonable to expect that weather events would react more to year-on-year swings, which sometimes exceeded 2ºF (1ºC), than to any ‘trend’.

The results show a strong observation bias in the recent events too – meaning all those NOAA reports since 1995 or 96. Using numbers:

  • The long-term method understates 2 event categories: lightning and heat. (Yes, NOAA tracks instances of ‘heat’)
  • Both methods are in agreement in another 10 events
  • For 17 events, the long-term method overstates

Put other way: for 17 event classes, the apparent ‘trend’ one could plot on a chart is probably overstating the real relationship between event counts and temperature. This rises to 19 if one includes the old events, hail and tornadoes.

(I excluded another event, high surf, as it shows a few hundred incidents per year – except for 2009, when there are over 13,000 occurrences. I’m not sure what to make of that. Besides, it almost always happens in Hawaii – and the NOAA temperatures I’m using refer only to the lower 48. To be strict I should have excluded all events happening in Hawaii and Alaska from the count, for the same reason, but it won’t make much difference; for example, in 2015 there were 57,000 events reported but these two states accounted for only about 1,000.)

Here I’m going to show some examples. I will not show every weather event because a) many of them are irrelevant and b) the post would have 64 charts.

Hail: okay, this event started to be reported in the 1950s so bias is to be expected. Still, just looking at the chart it’s obvious that the relatively recent increase has to be mostly due to expanded reporting. Does anyone think hail events multiplied by four or five in the nineties?

image

Looking at year-on-year changes the correlation coefficient (r2) is 0.069, or for all purposes zero.

image

Blizzards are another good example. The chart shows stable numbers or, if one excludes the first two years (which have very high figures), an increase.

image

Can higher temperatures be associated with more blizzards? In fact, the years in which temperatures increase have less blizzards, while those when temperatures decline have more of them (as common sense would indicate). The correlation since 1996 is -0.55 including all years (p-value = 0.011).

Here I show the plot excluding the first two years, for consistency. Still the correlation is -0.50 and the p-value is quite low (0.041), so the association between increased temperatures and decreased blizzards seems robust.

image

As for thunderstorms, there seems to be no correlation with temperatures (r2 = 0.039). But again a simple plot would appear to show an increase over time.

image

image

 

NOAA also tracks something it calls winter weather – really. I’m not sure what exactly they include here, but looking at a plot you’d think we’ve been seeing a lot more winter of late…

 

image

Obviously, the year-on-year chart shows a negative rather than positive relationship between winter and temperature. Correlation = -0.43, p value = -0.06.

image

Flash floods appear to be going through the roof…

image

… when in fact the relationship is negative, with a correlation of -0.32. (The p-value, 0.18, suggests this is just noise, i.e. no real relationship).

image

Wildfires also seem to be increasing:

image

But there is virtually no correlation (0.057).

image

Heavy rain is supposedly exploding too:

image

But the correlation is again negative: -0.08

image

Conclusions, and a question for readers

Using event counts is useless for most weather events. It may make sense for the largest (e.g. hurricanes) as these are unlikely to be affected by any reporting bias, but for wildfire, hail, tornadoes, and so on it’s dead wrong.

Another measure of the impact of weather events on the economy is needed. One such measure could be the proportion of losses as a percentage of GDP; if you follow the debate perhaps you’ve come across this chart, or a similar one.

Now, Roger Pielke plots insured losses, which makes sense if one wants to be more or less sure the losses are real (if not, one would have to assume the ‘losses’ are equal to whatever the government decides to spend after a disaster). But there are problems when using this data over a long time frame:

a) As the world develops, a greater share of assets will be insured. If insured losses are growing faster than overall losses, there will be an upward bias in the chart.

b) There aren’t convincing reasons to expect weather disasters, as a percentage of GDP, to be stable. If technology (buildings, early detection systems, etc.) improves, then perhaps we should expect it to decline. This will create a downward bias.

There are probably more biases that I cannot think of right now, but you get the point. The red line in that chart doesn’t necessarily mean the weather is becoming better over time, nor would it mean the weather is getting worse if it trended upwards. It simply means weather disasters cost less as a % of GDP, a fact that may or may not be due to better weather.

It occurs to me that using the year-on-year change in disaster losses is a way to get around the ‘drift’ or bias created by technology improvements, economic growth, etc. So my question is, does anyone know where the actual figures on weather-related losses are? The chart says the source is ‘Munich Re’ but I cannot find the numbers going back to 1990 on that website.

One last thought…

Whether weather disasters/events are increasing or decreasing seems to me an interesting question by itself. But the simple fact that there are more or less weather events does not mean one climate state is preferable to another.

After all, there must be pretty few weather events in Antarctica.


References:

NOAA event database here

NOAA temperatures here

Files as used, along with code, here

The files on NOAA’s webpage still seem to be missing a lot of info – including, crucially, the column that describes the event type. The files I uploaded to Dropbox do include that info, but they’re missing 2016 as I downloaded them a few months ago.

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April 10, 2017 at 12:01PM