energy Archives - Page 4 of 9

On the rebound

Grist covers a detailed report on the rebound effect, which recently appeared at ElectricityPolicy.com (pdf from NRDC). The report discusses a wide range of rebound arguments, basically concluding that rebounds are not a big deal.

Some of the reasons derive from the microeconomic effects of efficiency improvements. For example, improving the efficiency of light bulbs makes light services cheaper. But user’s don’t immediately increase lighting in proportion to the cost reduction, because their demand for lighting is saturated: there are only so many fixtures in a house, hours in the day requiring light, etc. Similarly, the elasticity of dirty dish production with respect to the energy cost of running a dishwasher is pretty darn low. This is reminiscent of the dynamics of process improvement at Analog Devices, where TQM improved productivity, but the company had a hard time translating that to expansion of its market niche in the short term.

I think the report underweights the long term effects of efficiency though. Efficiency increases contribute to aggregate productivity growth in the economy (more than you’d expect, if you believe that agency problems and other market failures create a bias toward overuse of energy). With wealth comes an expansion of energy use, hence the boom in such energy hogs as undercounter freezers and wine chillers, countering Energy Star improvement in refrigeration. However, this is not really an efficiency problem; it’s a progress problem, and it brings welfare benefits along with the added energy (at least until you get to the absurd margin).

The report cites an Energy Policy survey of empirical estimates:

Improvements in energy efficiency make energy services cheaper, and therefore encourage increased consumption of those services. This so-called direct rebound effect offsets the energy savings that may otherwise be achieved. This paper provides an overview of the theoretical and methodological issues relevant to estimating the direct rebound effect and summarises the empirical estimates that are currently available. The paper focuses entirely on household energy services, since this is where most of the evidence lies and points to a number of potential sources of bias that may lead the effect to be overestimated. For household energy services in the OECD, the paper concludes that the direct rebound effect should generally be less than 30%. doi:10.1016/j.enpol.2008.11.026

Sadly, a press release for related studies from the same research group spins this as a catastrophe:

‘Rebound Effects’ Threaten Success of UK Climate Policy

This is really only a catastrophe for a politician foolish enough to try to set and hit a hard emissions target, with efficiency mandates as the only measure for achieving it. As soon as you have any course correction (i.e. negative feedback) built into your policies, like an adaptive carbon tax or cap & trade system (the latter being the less stable option), the catastrophe goes away. The real catastrophe is failing to price GHG emissions and other externalities due to misperceptions about efficiency.

The real bottom line for rebound effects should be, “who cares?” If rebound effects are large, efficiency programs have small energy effects, but potentially large welfare improvements (if you accept that there are energy market failures tending towards overconsumption), and emissions pricing has large energy effects, because high rebound implies high price elasticity. If rebound effects are small, efficiency programs work and emissions pricing is a good way to collect taxes. Neither condition is a reason to avoid efficiency or emissions pricing, though emissions pricing is the preferable way to proceed.

Elasticity contradictions

If a global oil shock reduces supply 10%, the price of crude will rise to $20,000/barrel, with fuel expenditures consuming more than the entire GDP of importing nations.

At least that’s what you’d predict if you think the price elasticity of oil demand is about -0.02. I saw that number in a Breakthrough post, citing Kevin Drum, citing Early Warning, citing IMF. It’s puzzling that Breakthrough is plugging small price elasticities here, when their other arguments about the rebound effect require elasticities to have large magnitudes. Continue reading “Elasticity contradictions”

The real constraint on nuclear power: war

A future where everything goes right for nuclear power, with advancing technology driving down costs, making reactors a safe and ubiquitous energy source, and providing a magic bullet for climate change, might bring other surprises.

For example, technology might also make supersonic cruise missiles cheap and ubiquitous.

The Fukushima operators appear to be hanging in there. But imagine how they’d be coping if someone fired a missile at them once in a while.

Fortunately, reactors today are mostly in places where peace and rule of law prevail.

world_map

But peace and good governance aren’t exactly the norm in places where emissions are rising rapidly, or the poor need energy.

Building lots of nuclear power plants is ultimately a commitment to peace, or at least acceptance of rather dreadful consequences of war (not necessarily war with nuclear weapons, but war with conventional weapons turning nuclear reactors into big dirty bombs).

One would hope that abundant, clean energy would reduce the motivation to blow things up, but how much are we willing to gamble on that?

A System Zoo

I just picked up a copy of Hartmut Bossel’s excellent System Zoo 1, which I’d seen years ago in German, but only recently discovered in English. This is the first of a series of books on modeling – it covers simple systems (integration, exponential growth and decay), logistic growth and variants, oscillations and chaos, and some interesting engineering systems (heat flow, gliders searching for thermals). These are high quality models, with units that balance, well-documented by the book. Every one I’ve tried runs in Vensim PLE so they’re great for teaching.

I haven’t had a chance to work my way through the System Zoo 2 (natural systems – climate, ecosystems, resources) and System Zoo 3 (economy, society, development), but I’m pretty confident that they’re equally interesting.

You can get the models for all three books, in English, from the Uni Kassel Center for Environmental Systems Research – it’s now easy to find a .zip archive of the zoo models for the whole series, in Vensim .mdl format, on CESR’s home page: www2.cesr.de/downloads.

To tantalize you, here are some images of model output from Zoo 1. First, a phase map of a bistable oscillator, which was so interesting that I built one with my kids, using legos and neodymium magnets:

Continue reading “A System Zoo”

Production functions – so pretty, so unphysical

I’m rediscovering my old frustrations with aggregate production functions like the CES. They’re handy, but I have a nagging suspicion, never quite formalized, that they just don’t capture the engineering/thermodynamic realities of substitution. Anyone know any papers on that? I’m aware of critiques of KLEM applications, but not interfuel aggregation.

Click to enlarge. From a google images search for production function.

Dynamics of Fukushima Radiation

I like maps, but I love time series.

ScienceInsider has a nice roundup of radiation maps. I visited a few, and found current readings, but got curious about the dynamics, which were not evident.

So, I grabbed Marian Steinbach’s scraped data and filtered it to a manageable size. Here’s what I got for the 9 radiation measurement stations in Ibaraki prefecture, where the Fukushima-Daiichi reactors are located:

The time series above (click it to enlarge) shows about 10 days of background readings, pre-quake, followed by some intense spikes of radiation, with periods of what looks like classic exponential decay behavior. “Intense” is relative, because fortunately those numbers are in nanoGrays, which are small.

The cumulative dose at these sites is not yet high, but climbing:

The Fukushima contribution to cumulative dose is about .15 milliGrays – according to this chart, roughly a chest x-ray. Of course, if you extrapolate to long exposure from living there, that’s not good, but fortunately the decay process is also underway.

The interesting thing about the decay process is that it shows signs of having multiple time constants. That’s exactly what you’d expect, given that there’s a mix of isotopes with different half lives and a mix of processes (radioactive decay and physical transport of deposited material through the environment).

The linear increases in the time constant during the long, smooth periods of decay presumably arise as fast processes play themselves out, leaving the longer time constants to dominate. For example, if you have a patch of soil with cesium and iodine in it, the iodine – half life 8 days – will be 95% gone in a little over a month, leaving the cesium – half life 30 years – to dominate the local radiation, with a vastly slower rate of decay.

Since the longer-lived isotopes will dominate the future around the plant, the key question then is what the environmental transport processes do with the stuff.

Update: Here’s the Steinbach data, aggregated to hourly (from 10min) frequency, with -888 and -888 entries removed, and trimmed in latitude range. Station_data Query hourly (.zip)

Nuclear systems thinking roundup

Mengers & Sirelli call for systems thinking in the nuclear industry in IEEE Xplore:

Need for Change Towards Systems Thinking in the U.S. Nuclear Industry

Until recently, nuclear has been largely considered as an established power source with no need for new developments in its generation and the management of its power plants. However, this idea is rapidly changing due to reasons discussed in this study. Many U.S. nuclear power plants are receiving life extensions decades beyond their originally planned lives, which requires the consideration of new risks and uncertainties. This research first investigates those potential risks and sheds light on how nuclear utilities perceive and plan for these risks. After that, it examines the need for systems thinking for extended operation of nuclear reactors in the U.S. Finally, it concludes that U.S. nuclear power plants are good examples of systems in need of change from a traditional managerial view to a systems approach.

In this talk from the MIT SDM conference, NRC commissioner George Apostolakis is already there:

Systems Issues in Nuclear Reactor Safety

This presentation will address the important role system modeling has played in meeting the Nuclear Regulatory Commission’s expectation that the risks from nuclear power plants should not be a significant addition to other societal risks. Nuclear power plants are designed to be fundamentally safe due to diverse and redundant barriers to prevent radiation exposure to the public and the environment. A summary of the evolution of probabilistic risk assessment of commercial nuclear power systems will be presented. The summary will begin with the landmark Reactor Safety Study performed in 1975 and continue up to the risk-informed Reactor Oversight Process. Topics will include risk-informed decision making, risk assessment limitations, the philosophy of defense-in-depth, importance measures, regulatory approaches to handling procedural and human errors, and the influence of safety culture as the next level of nuclear power safety performance improvement.

The presentation is interesting, in that it’s about 20% engineering and 80% human factors. Figuring out how people interact with a really complicated control system is a big challenge.

This thesis looks like an example of what Apostolakis is talking about:

Perfect plant operation with high safety and economic performance is based on both good physical design and successful organization. However, in comparison with the affection that has been paid to technology research, the effort that has been exerted to enhance NPP management and organization, namely human performance, seems pale and insufficient. There is a need to identify and assess aspects of human performance that are predictive of plant safety and performance and to develop models and measures of these performance aspects that can be used for operation policy evaluation, problem diagnosis, and risk-informed regulation. The challenge of this research is that: an NPP is a system that is comprised of human and physics subsystems. Every human department includes different functional workers, supervisors, and managers; while every physical component can be in normal status, failure status, or a being-repaired status. Thus, an NPP’s situation can be expressed as a time-dependent function of the interactions among a large number of system elements. The interactions between these components are often non-linear and coupled, sometime there are direct or indirect, negative or positive feedbacks, and hence a small interference input either can be suppressed or can be amplified and may result in a severe accident finally. This research expanded ORSIM (Nuclear Power Plant Operations and Risk Simulator) model, which is a quantitative computer model built by system dynamics methodology, on human reliability aspect and used it to predict the dynamic behavior of NPP human performance, analyze the contribution of a single operation activity to the plant performance under different circumstances, diagnose and prevent fault triggers from the operational point of view, and identify good experience and policies in the operation of NPPs.

The cool thing about this, from my perspective, is that it’s a blend of plant control with classic SD maintenance project management. It looks at the plant as a bunch of backlogs to be managed, and defines instability as a circumstance in which the rate of creation of new work exceeds the capacity to perform tasks. This is made operational through explicit work and personnel stocks, right down to the matter of who’s in charge of the control room. Advisor Michael Golay has written previously about SD in the nuclear industry.

Others in the SD community have looked at some of the “outer loops” operating around the plant, using group model building. Not surprisingly, this yields multiple perspectives and some counterintuitive insights – for example:

Regulatory oversight was initially and logically believed by the group to be independent of the organization and its activities. It was therefore identified as a policy variable.

However in constructing the very first model at the workshop it became apparent that for the event and system under investigation the degree of oversight was influenced by the number of event reports (notifications to the regulator of abnormal occurrences or substandard conditions) the organization was producing. …

The top loop demonstrates the reinforcing effect of a good safety culture, as it encourages compliance, decreases the normalisation of unauthorised changes, therefore increasing vigilance for any outlining unauthorised deviations from approved actions and behaviours, strengthening the safety culture. Or if the opposite is the case an erosion of the safety culture results in unauthorised changes becoming accepted as the norm, this normalisation disguises the inherent danger in deviating from the approved process. Vigilance to these unauthorised deviations and the associated potential risks decreases, reinforcing the decline of the safety culture by reducing the means by which it is thought to increase. This is however balanced by the paradoxical notion set up by the feedback loop involving oversight. As safety improves, the number of reportable events, and therefore reported events can decrease. The paradoxical behaviour is induced if the regulator perceives this lack of event reports as an indication that the system is safe, and reduces the degree of oversight it provides.

Tsuchiya et al. reinforce the idea that change management can be part of the problem as well as part of the solution,

Markus Salge provides a nice retrospective on the Chernobyl accident, best summarized in pictures:

Key feedback structure of a graphite-moderated reactor like Chernobyl

“Flirting with Disaster” dynamics

Others are looking at the nuclear fuel cycle and the role of nuclear power in energy systems.

How to be confused about nuclear safety

There’s been a long running debate about nuclear safety, which boils down to, what’s the probability of significant radiation exposure? That in turn has much to do with the probability of core meltdowns and other consequential events that could release radioactive material.

I asked my kids about an analogy to the problem: determining whether a die was fair. They concluded that it ought to be possible to simply roll the die enough times to observe whether the outcome was fair. Then I asked them how that would work for rare events – a thousand-sided die, for example. No one wanted to roll the dice that much, but they quickly hit on the alternative: use a computer. But then, they wondered, how do you know if the computer model is any good?

Those are basically the choices for nuclear safety estimation: observe real plants (slow, expensive), or use models of plants.

If you go the model route, you introduce an additional layer of uncertainty, because you have to validate the model, which in itself is difficult. It’s easy to misjudge reactor safety by doing five things:

Ignore the dynamics of the problem. For example, use a statistical model that doesn’t capture feedback. Presumably there have been a number of reinforcing feedbacks operating at the Fukushima site, causing spillovers from one system to another, or one plant to another:
- Collateral damage (catastrophic failure of part A damages part B)
- Contamination (radiation spewed from one reactor makes it unsafe to work on others)
- Exhaustion of common resources (operators, boron)
Ignore the covariance matrix. This can arise in part from ignoring the dynamics above. But there are other possibilities as well: common design elements, or colocation of reactors, that render failure events non-independent.
Model an idealized design, not a real plant: ignore components that don’t perform to spec, nonlinearities in responses to extreme conditions, and operator error.
Draw a narrow boundary around the problem. Over the last week, many commentators have noted that reactor containment structures are very robust, and explicitly designed to prevent a major radiation release from a worst-case core meltdown. However, that ignores spent fuel stored outside of containment, which is apparently a big part of the Fukushima hazard now.
Ignore the passage of time. This can both help and hurt: newer reactor designs should benefit from learning about problems with older ones; newer designs might introduce new problems; life extension of old reactors introduces its own set of engineering issues (like neutron embrittlement of materials).
Ignore the unknown unknowns (easy to say, hard to avoid).

I haven’t read much of the safety literature, so I can’t say to what extent the above issues apply to existing risk analyses based on statistical models or detailed plant simulation codes. However, I do see a bit of a disconnect between actual performance and risk numbers that are often bandied about from such studies: the canonical risk of 1 meltdown per 10,000 reactor years, and other even smaller probabilities on the order of 1 per 100,000 or 1,000,000 reactor years.

I built myself a little model to assess the data, using WNA data to estimate reactor-years of operation and a wiki list of accidents. One could argue at length which accidents should be included. Only light water reactors? Only modern designs? I tend to favor a liberal policy for including accidents. As soon as you start coming up with excuses to exclude things, you’re headed toward an idealized world view, where operators are always faithful, plants are always shiny and new, or at least retired on schedule, etc. Still, I was a bit conservative: I counted 7 partial or total meltdown accidents in commercial or at least quasi-commercial reactors, including Santa Susana, Fermi, TMI, Chernobyl, and Fukushima (I think I missed Chapelcross). Then I looked at maximum likelihood estimates of meltdown frequency over various intervals. Using all the data, assuming Poisson arrivals of meltdowns, you get .6 failures per thousand reactor-years (95% confidence interval .3 to 1). That’s up from .4 [.1,.8] before Fukushima. Even if you exclude the early incidents and Fukushima, you’re looking at .2 [.04,.6] meltdowns per thousand reactor years – twice the 1-per-10,000 target. For the different subsets of the data, the estimates translate to an expected meltdown frequency of about once to thrice per decade, assuming continuing operations of about 450 reactors. That seems pretty bad.

In other words, the actual experience of rolling the dice seems to be yielding a riskier outcome than risk models suggest. One could argue that most of the failing reactors were old, built long ago, or poorly designed. Maybe so, but will we ever have a fleet of young rectors, designed and operated by demigods? That’s not likely, but surely things will get somewhat better with the march of technology. So, the question is, how much better? Areva’s 10x improvement seems inadequate if it’s measured against the performance of existing plants, at least if we plan to grow the plant fleet by much more than a factor of 10 to replace fossil fuels. There are newer designs around, but they depart from the evolutionary path of light water reactors, which means that “past performance is no indication of future returns” applies – will greater passive safety outweigh the effects of jumping to a new, less mature safety learning curve?

It seems to me that we need models of plant safety that square with the actual operational history of plants, to reconcile projected risk with real-world risk experience. If engineers promote analysis that appears unjustifiably optimistic, the public will do what it always does: discount the results of formal models, in favor of mental models that may be informed by superstition and visions of mushroom clouds.

Nuclear safety follies

I find panic-fueled iodine marketing and disingenuous comparisons of Fukushima to Chernobyl deplorable.

But those are balanced by pronouncements like this:

Telephone briefing from Sir John Beddington, the UK’s chief scientific adviser, and Hilary Walker, deputy director for emergency preparedness at the Department of Health.“Unequivocally, Tokyo will not be affected by the radiation fallout of explosions that have occurred or may occur at the Fukushima nuclear power stations.”

Surely the prospect of large scale radiation release is very low, but it’s not approximately zero, which is my interpretation of “unequivocally not.”

On my list of the seven deadly sins of complex systems management, number four is,

Certainty. Planning for it leads to fragile strategies. If you can’t imagine a way you could be wrong, you’re probably a fanatic.

Nuclear engineers disagree, but some seem to have a near-fanatic faith in plant safety. Normal Accidents documents some bizarrely cheerful post-accident reflections on safety. I found another when reading up over the last few days:

Continue reading “Nuclear safety follies”

Will complex designs win the nuclear race?

Areva pursues “defense in depth” for reactor safety:

Areva SA (CEI) Chief Executive Officer Anne Lauvergeon said explosions at a Japanese atomic power site in the wake of an earthquake last week underscore her strategy to offer more complex reactors that promise superior safety.

“Low-cost reactors aren’t the future,” Lauvergeon said on France 2 television station yesterday. “There was a big controversy for one year in France about the fact that our reactors were too safe.”

Lauvergeon has been under pressure to hold onto her job amid delays at a nuclear plant under construction in Finland. The company and French utility Electricite de France SA, both controlled by the state, lost a contract in 2009 worth about $20 billion to build four nuclear stations in the United Arab Emirates, prompting EDF CEO Henri Proglio to publicly question the merits of Areva’s more complex and expensive reactor design.

Areva’s new EPR reactors, being built in France, Finland and China, boasts four independent safety sub-systems that are supposed to reduce core accidents by a factor 10 compared with previous reactors, according to the company.

The design has a double concrete shell to withstand missiles or a commercial plane crash, systems designed to prevent hydrogen accumulation that may cause radioactive release, and a core catcher in the containment building in the case of a meltdown. To withstand severe earthquakes, the entire nuclear island stands on a single six-meter (19.6 feet) thick reinforced concrete base, according to Paris-based Areva.

– via Bloomberg

I don’t doubt that the Areva design is far better than the reactors now in trouble in Japan. But I wonder if this is really the way forward. Big, expensive hardware that uses multiple redundant safety systems to offset the fundamentally marginal stability of the reaction might indeed work safely, but it doesn’t seem very deployable on the kind of scale needed for either GHG emissions mitigation or humanitarian electrification of the developing world. The financing comes in overly large bites, huge piles of concretes increase energy and emission payback periods, and it would take ages to ramp up construction and training enough to make a dent in the global challenge.

I suspect that the future – if there is one – lies with simpler designs that come in smaller portions and trade some performance for inherent stability and antiproliferation features. I can’t say whether their technology can actually deliver on the promises, but at least TerraPower – for example – has the right attitude:

“A cheaper reactor design that can burn waste and doesn’t run into fuel limitations would be a big thing,” Mr. Gates says.

However, even simple/small-is-beautiful may come rather late in the game from a climate standpoint:

While Intellectual Ventures has caught the attention of academics, the commercial industry–hoping to stimulate interest in an energy source that doesn’t contribute to global warming–is focused on selling its first reactors in the U.S. in 30 years. The designs it’s proposing, however, are essentially updates on the models operating today. Intellectual Ventures thinks that the traveling-wave design will have more appeal a bit further down the road, when a nuclear renaissance is fully under way and fuel supplies look tight. – Technology Review

Not surprisingly, the evolution of the TerraPower design relies on models,

Myhrvold: When you put a software guy on an energy project he turns it into a software project. One of the reasons were innovating around nuclear is that we put a huge amount of energy into computer modeling. We do very extensive computer modeling and have better computer modeling of reactor internals than anyone in the world. No one can touch us on software for designing the reactor. Nuclear is really expensive to do experiments on, so when you have good software it’s way more efficient and a shorter design cycle.

Computing is something that is very important for nuclear. The first fast reactors, which TerraPower is, were basically designed in the slide rule era. It was stunning to us that the guys back then did what they did. We have these incredibly accurate simulations of isotopes and these guys were all doing it with slide rules. My cell phone has more computing power than the computers that were used to design the world’s nuclear plants.

It’ll be interesting to see whether current events kindle interest in new designs, or throw the baby out with the bathwater (is it a regular baby, or a baby Godzilla?). From a policy standpoint, the trick is to create a level playing field for competition among nuclear and non-nuclear technologies, where government participation in the fuel cycle has been overwhelming and risks are thoroughly socialized.