The Renewables Hump 6: EROEI of Solar and Wind

As my brief pause from posting may have suggested, I'm struggling with how to best tackle the next step in this series. To recap, so far I've been discussing the "true" EROEI of renewable energy sources--meaning the measurement with no artificial boundary for accounting--and the need and challenges for calculating this figure. My plan was: 1) work through a few price-estimated EROEI calculations (at least one recent solar and one recent wind project); 2) show that these price-estimated EROEI figures are too low to support envisioned transitions to renewable energy sources; and then 3) argue that, while this method of calculating EROEI is itself suspect, until we come up with a better method for calculating boundaryless "true" EROEI, we must seriously scrutinize the viability of the predominant transition vision.

I've been having a difficult time figuring out how to best make this into a solid argument. My initial desire was to use a very numbers-driven approach. I never intended price-estimated-EROEI to provide some verifiable, "true" EROEI figure, however (it is intended more as a reality-check backstop), so I've been concerned with proceeding on such a numbers-driven approach with price-estimated EROEI as the foundation. To be honest, I was hoping that, in writing this series, I would arrive at some far more accurate and transparent means of calculating "true" EROEI. Unfortunately, the result has been the opposite--while I am still convinced of the value of price-estimated-EROEI as a reality check, its inherent flaws have been well highlighted by my own efforts to refine it, and especially by the very thoughtful comments that I've received.

Therefore, I now think it's best to embrace this fuzziness--to use the price-estimated-EROEI in its originally intended role of reality check. While I'm concerned that my critique of the "renewables transition" will now be more logic-driven and less hard numbers-driven, I am increasingly OK with this shift (a decision process which, in part, explains my lack of recent posting). I've arrived at this conclusion because a logic-driven approach actually seems more true to form: I've been arguing for some time that "true" EROEI is fundamentally impossible from a nuts-and-bolts accounting perspective. Instead, we must use proxies to measure emergent phenomena--such as market price--to estimate its value.

That said, here's my current plan: Below I'll outline my calculations of the price-estimated-EROEI of one recent solar project and one (less) recent wind project. Next week, I'll argue the impacts--and uncertainties--of these numbers on the viability of the "renewables transition." Following that, I'll address a number of ancillary issues: an analysis of the minimum EROEI for society (drawing on, but to some extent disagreeing with Hall's work on the same topic), a discussion of the carbon-impact of the "renewables transition," and I'll conclude with, hopefully, some general guidelines for going forward amidst this uncertainty.

Solar Example: Downtown LA Solar PV Installation: This 2009 installation is my example for price-estimated EROEI calculation. I think it's a good example (no example is perfect) for several reasons: at 1.2 MW, it's modest in size, but large enough to reap economies of scale; because it is installed on an existing roof space, there is no land cost associated with the installation (that, in some circumstances, could present acquisition costs or environmental compliance/impact statment costs not truly representative of net energy issues); because it is in California, where the average cost of electricity (and especially peaking "sunny day" electricity that solar provides) is higher, it will provide a more conservative estimate; because it is located in the downtown of a major metropolitan area it will not require significant transmission investment to provide a true measure, and is therefore also more conservative. Finally, there are good cost and output numbers available for the site.

Basic data: 1.2 MW array installed 2009 in Los Angeles, cost $16.5 million up front (ignoring rebates/tax credits/incentives), projected financial return of $550,000 per year. At the rough California rate of $.15 per KWh, that's about 4 GWh per year (conservative).

Price-Estimated-EROEI Calculation: The $16.5 million up-front is, at $0.09/KWh (here using national average, as there's no reason to think that manufacturers would use primarily California peaking power to build this system), an input of 183 GWh through installation (I'm ignoring the realtively small maintenance costs here, which will also make the figure more conservative). If we assume a life-span of 40 years, then the energy output of this system is 160 GWh. That's a price-estimated EROEI of 0.87:1.

Wind Example: I've had a more difficult time finding a recent wind project where good data (on both cost and actual, as opposed to nameplate, output) is available. As a result, I've chosen a 2000 Danish offshore wind project at Middelgrunden. While up-front expenses may be higher off-shore (making the resulting EROEI more accurate for offshore projects than on-shore), I think this is a relatively modern installation (2MW turbines). If readers have more current projects with full data, please provide in the comments--another point for investigation is whether the price-estimated-EROEI of solar and wind have been improving or if they are holding relatively stead.

Basic data: Cost of $60 million, annual energy ouput 85 GWh.

Price-Estimated-EROEI Calculation: At the US national average rate for electricity ($0.09/KWh), the $60 million up-front energy investment works out to 666 GWh. Using a life-span of 25 years (and assuming zero maintenance, grid, or storage investment, making the result artificially high), the energy output comes to 2125 GWH. That's a price-estimated-EROEI of 3.2:1.

I'll let everyone chew on these numbers--and the various issues surrounding how they were derived--for the week. If you have access to similar numbers for other solar or wind projects (or numbers for tidal or geothermal), please provide them in the comments and we'll see if we can generate more figures. Next week I'll discuss the impact--and uncertainty--of these calculations.

Resilient Suburbia Mention

I'm a bit behind the time, but I just learned today while doing an interview on renewable energy transition that Bill McKibben recently mentioned my Resilient Suburbia series.  McKibben writes:

Though our sprawl is designed for the car, the sunk costs of those tens of millions of houses mean they're not going to disappear just because the price of gas rises. They'll have to change instead. "Suburbia, not as a model for material consumption, but as a legal and social lattice of decentralized and more uniformly distributed production land ownership, has the potential to serve as the foundation for just such a pioneering adaptation," writes Jeff Vail, a widely read economic theorist who envisions "a Resilient Suburbia."

New Oil Drum Post: Afghan Mineral Geopolitics -- Cure, Curse, or Hype?

I've just posted a new article to The Oil Drum discussing geopolitical concerns about the recent Afghan mineral "discovery."

The Renewables Hump 5: Proxy EROEI Measurement

Today's installment will address two of the potential proxies for calculating "true" EROEI (meaning a calculation with no artificial system boundary) for renewable energy sources.  Last week's post discussed the price-estimated EROEI theory, and this week I'll discuss two additional potential proxies.  Up front, it's important to note that, much like the price-estimated theory, these proxies have significant weaknesses.  I don't think either is yet ready for use, but talking through them helps to better define the issues surrounding proxy-calculation of EROEI, and may result in readers figuring out how we CAN make ideas like these work...

Asymptote location model:

The first proxy I'll discuss is--for lack of a better name--the asymptote location model.  I'll start by noting that I think this model is still too incomplete to be workable.  I include it here because I think it may be fertile ground for someone to develop.  Here's the basic concept:  under traditional EROEI calculations, there is an artificial system boundary drawn at some point, and the result is an artificially high EROEI (because those energy inputs outside that artificial boundary are not counted).  My theory starts with the assumption that, as that system boundary is expanded, the resulting EROEI will approach some theoretical "true" EROEI that lies at an infinite, but uncomputable, system boundary.  This is a classic example of an asymptote.  If we can plot the degree of system boundary expansion on the Y axis, and the resulting EROEI value on the X axis, then X will approach the true EROEI value as Y approaches infinity (unbounded EROEI calculation).  This, in theory, will allow us to fit an equation to a few points (which can be calculated) and deduce the location of the asymptote that represents "true" EROEI without actually needing to perform the impossible calculation of the unbounded EROEI analysis.  The problem, of course, is that while it's quite possible to fit that X-value (EROEI) into a meaningful scale, but the same can't quite so easily be said about the Y-value.  What does a "5" on the Y-axis (degree of system boundary expansion) mean compared to a "10"?  What is the scale?  Unless an 8 is double a 4 is double a 2 in some meaningful sense, the equation fit to locate the asymptote will provide a meaningless result.  Can we create a meaningful scale for the Y-axis?  Maybe.  It seems possible to use the number of steps of regression (as in 1=just the energy used at the plant and installation, 2= 1 plus the energy used to create everything in the plant/installation, etc.) as a scale, but this is just speculation.  This might be fertile ground for someone looking to develop the field of EROEI analysis, but it's not ready for prime-time at this point.  I'm very interested in any ideas readers may have about turning this rough idea into a workable proxy measurement.

Worker-year model:

Neil Howes sent me a a very interesting calculation for wind energy that used the ratio of worker-years involved in the wind industry to total US worker-years as a means to determine what portion of total US energy consumption was required as input to US-produced wind capacity.  His EROEI measurement came out at over 100:1, and I think significantly overestimates the true ratio because, like most EROEI calculations, it artificially limited the system boundary quite severely (for example, it based its worker-year number on a DOE study that estimated the number of wind-energy jobs that may be created for a set amount of production--this didn't include all the supporting industry jobs that would be created in mining, transportation, marketing, finance, training, etc.).   Additionally, I think that the brute-force methods for calculating EROEI (input/output and process analysis) necessarily represent an upper bound to the "true" EROEI--they accurately count energy output, and are universally low (to an unknown extent) on their accounting for energy input.  As a result, any proxy that estimates higher than the brute-force approach must be reconsidered.

The far more significant inaccuracy is that this methodology assumes a uniformity of the very EROEI it attempts to measure.  The measurement is only accurate IF every worker-year can act as a proxy for an equal amount of US annual energy consumption--it can't.  Instead, some workers (and their associated industrial/commercial processes) represent far more energy than others.  This lays bare the problem with this methodology:  it would come back with the same energy input for 1000 worker-years on a 50:1 EROEI oil well as for 1000 worker-years on a 3:1 EROEI solar plant, even where the energy generation capacity of each is the same--the energy input is not necessarily the same.  I would argue that the energy input could be seen as the same IF we took a boundary-less approach to attributing worker-years, but then we get back to our overarching accounting problem.

I think that these two proxy-methodologies outlined above both present some potential for development, but neither is yet ready for actual use.  They both present novel approaches to the proxy-calculation of EROEI, but seem to me unacceptably ill-defined--both when compared to brute-force EROEI calculations and when compared to the price-estimated theory of proxy-EROEI calculation.  

Going forward, I'll look at both solar and wind, and I'll present a survey of traditional EROEI calculations as well as proxy calculations based on the price-estimated model.  If readers have any thoughts on other proxy-methodologies to use (or how to make the asymptote or worker-year methods work), please let me know.

Thoughts on Rick "Duncan" Strandlof

A little current-affairs bit that seems worth interrupting my Renewables Hump series (still hope to get a new post in the series up Monday):

I was on vacation from June 4-10, and had the chance to read the paper edition of the New York Times cover to cover each day. I was amused to see an article on a Colorado veterans advocate named Rick Duncan (here's a CNN article with no pay-wall). Turns out that Mr. Duncan, actually Mr. Strandlof, was a fraud--albeit a very successful and high profile one.

About two months ago I had lunch with Mr. Strandlof and LtCol John Flerlage (running for Congress in Colorado's 6th district, and certainly not a fraud). Ducan/Strandlof set up the meeting with the intent that I help LtCol Flerlage develop his energy and energy geopolitics platform. While I didn't speak with Strandlof for more than a minute or two, he seemed very sincere and interested--the same qualities, I'm sure, that brought him to a position of influence in Colorado politics.

While I disagree with Strandlof's fraud, my only first-hand impression of him was that he was doing important work. I won't profess that I understand what was going on in his head--we have more than enough media personalities who will happily discuss that without any real insight. What I can say is this: as a veteran, and as someone who worked (though very briefly) with Strandlof, I don't feel in any way hurt by his actions. Other than my assumption that the veterans energy policy conference he was setting up won't materialize as a result of his lies, his brief legacy is more likely to be increased awareness of veterans issues--something I really can't argue with.

The Renewables Hump 4: EROEI Issues

As discussed in the last post in this series, the energy return on energy invested in renewable sources of energy will be a critical measure of whether it is possible to transition on a large scale from a fossil-fuel powered economy, or whether a global "powerdown" is eventually inevitable.  If EROEI, the net-energy ratio of a renewable energy source, is high--say 40:1--then it should be possible to rapidly transition our fossil-fuel driven economy to a renewable energy base, and to support ongoing economic growth that requires ever more energy.  If this ratio, however, is low--say 4:1--then at a minimum a transition to renewable energy will be extremely challenging, and may be effectively impossible.  As a result, the actual EROEI value of the various renewable energy options available to us is plainly critical.  There are lots of measures, lots of studies, and lots of figures floated about for the EROEI value of solar photovoltaics, wind turbines, etc.  There is not, however, a universally accepted methodology for calculating EROEI.  In fact, I don't think it's a stretch to say that EROEI figures are more likely to be marketing copy intended to secure venture capital than the result of rigorous inquiry.  

In my opinion, understanding the reality of our society's ability to transition to a renewable energy basis for our economy is one of, if not THE most important issue to be resolved.  If this transition is a realistic possibility, then it should be our society's primary and immediate focus.  In addition, improving our understanding of just how realistic such a societal transition is will help us understand the necessary rate of investment in renewables, as well as the nature and degree of the challenges to be accomplished.  If it is not realistic, then we must not waste what little surplus energy we have on a fools errand.  In addition, the present understanding that such a transition is unrealistic will allow us to both develop and focus on those societal options that are realistic.  Given the importance of accurate EROEI calculations, this post will discuss the current methodology issues with EROEI calculation and make recommendations for proceeding.

There are two generally used methods for calculating EROEI:  process-analysis and input-output analysis.  Both basically boil down to a brute-force accounting of energy used in various component processes of producing a renewable energy source, with the key differences being how wide a net is cast in counting energy inputs.  For example, is the diesel fuel required to deliver the turbine blades to the installation site accounted for?  What about the energy required to build the truck, divided by the percentage of that truck's useful life used in that delivery?  What about fraction of the energy required to build the machine tools used in the manufacture of that truck?  

This highlights the problem main problem with current system boundary calculations:  you can regress these energy inputs infinitely far (e.g. what about the energy used to grow the rice eaten by the merchant marine captain who piloted the ship that delivered the metal ores used in manufacturing the bolts that hold together the turbine tower), and it's fundamentally impossible to use a brute-force accounting methodology to account for all energy inputs.  If one hopes to use such a brute force approach (as used in both process-analysis and input-output analysis methods of EROEI calculation), then one must draw an artificial boundary for what is counted, and what is not.  Is it acceptable to artificially constrain the accounted system?  Clearly any artificial system boundary results in an artificially high EROEI, but how artificially high?  Does this long-tail of non-accounted-for system inputs make the resulting EROEI figure 1% too high?  10%?  100%?  10 times too high?  It's easy to dismiss, but how do we know if we are completely ignoring these long-tail energy inputs?  I think there's great cause for concern that our EROEI is significantly over-estimated.  For example, in a paper by Prof. Cutler Cleveland and others, the EROEI of wind-power is assessed by looking at over 100 separate EROEI studies.  These studies are broken down into process-analysis and input-output methodologies.  Prof. Cleveland notes that process-analysis generally draws a tighter system boundary than input-output analysis--that is, it counts fewer inputs.  In that survey, the process-analysis EROEI measurements for wind average 24:1, and the input-output measurements average 12:1.  That's a 100% difference based on where the artificial system boundary is drawn.  In light of that significant difference, how can we be sure that a truly inclusive system boundary wouldn't result in a further 100% (or more) decrease in the measured EROEI?  The take-away here is that we simply can't trust the accuracy of currently available EROEI calculations.  Further, it seems unreasonable to place any credence in any brute-force (e.g process-analysis or input-output analysis) approach to EROEI calculation.

How can we get around the accounting difficulties and arrive at an accurate EROEI calculation--a calculation that can do more than just provide a comparison between renewables, and can actually provide a self-contained assessment of whether a given technology can facilitate a societal energy-transition?  Odum has proposed what he calls an "Emergy" measurement that intends to account for a true EROEI measurement.  However, while Odum recognizes the importance of an inclusive calculation, Odum's methodology does nothing to address these accounting issues, and the end result is still a brute-force estimate that suffers from the same methodological failings as traditional EROEI calculations (even if it tends to arrive at lower EROEI figures).  

Rather than a brute-force approach that literally attempts to count all the energy inputs, I think it will be necessary to use a proxy to calculate "true" EROEI.  One methodology that I've proposed for this task is to use price as a proxy for EROEI.  I'll discuss briefly the theory of how this would work, as well as the clear problems with this approach.

It always struck me as fishy that various EROEI claims (especially for wind) result in an energy payback time of less than a year.  In other words, these figures suggest that it would only take a few months to pay back all the energy required to build a wind turbine, and then that wind turbine would go on generating electricity for decades more.  Why, then, didn't we already transition the vast majority of our energy base to wind if it's so efficient?  The answer is that the financial payback isn't nearly so rosy.  What accounts for the difference between the rapid energy payback (only months) and the much longer financial payback (often an order of magnitude or more longer)?  Intuitively, it seems that at least part of the answer is that the EROEI wasn't accounting for many inputs that were counted in the financial analysis.  For example, the financial analysis accounted for the high salaries--derivatives of the long years of training--that must be paid to the engineers, the financiers, the technicians, the managers, the materials scientists, etc. that are involved in the production of a wind turbine.  These long years of education certainly represent an energy input, but aren't accounted for in either process-analysis or input-output analysis EROEI calculations.  Similarly, the cost of raw materials represents, at least in theory, the full spectrum of energy, machinery, personnel, and support systems needed to extract, refine, transport, and market it--a great deal of which lies outside the traditional artificial system boundaries drawn in traditional EROEI calculations.  It seemed to me that the financial cost of a renewable was a better proxy for the energy inputs to that renewable than were any of the accepted EROEI calculation methodologies.  This is the core of what I've called "price-estimated EROEI," which uses financial cost as a proxy for energy cost.  The basic calculation assumes that the entire cost of a renewable is made up--eventually, if one regresses far enough--by energy, so divides that cost by an average energy cost to arrive at the energy input, and then compares that as a ratio to the amount of energy the renewable will produce over its lifetime.  Not surprisingly, this form of calculation tends to produce a far lower EROEI than any of the accepted EROEI methodologies.

Of course, there are acknowledged flaws with this price-estimated EROEI methodology.  Just to name a few, it's difficult to account for the differing values of the various types of input energies and the resulting output energy; there are market distortions, tax-incentive distortions, geopolitical distortions, etc.  That said, I think this type of proxy calculation at least directly addresses the need to calculate a truly inclusive EROEI, and may well be much closer to the "truth" of the required energy inputs than any traditional methodology.

In the next two post I'll address two other potential methods for measuring "true" EROEI:  asymptote location and worker-year calculation (as suggested by Neil Howes).  Then, I'll look at the EROEI of wind power and solar power from both traditional and proxy methods of calculation.

The Renewables Hump 3: The Target

In he last post in this series, I discussed the criticality of accurately measuring EROEI of potential alternative energy technologies.  If the EROEI of a renewable energy is high enough, then a relatively small initial investment of energy can lead to the rapid scale-up of renewable generation by bootstrapping its own energy production to finance (in energy terms) its own growth.  However, if EROEI is too low, then the amount of energy that society must invest to meet a renewable target would be so great as to be effectively impracticable (because it would cause sufficient energy price spikes as to threaten so much immediate economic damage as to be politically impossible).

Before proceeding with this discussion of EROEI, I thought it would be worth defining what this target for a renewable transition actually looks like:

First, it's important to recognize that there are a variety of possible targets.  Some include: a general transition target (either total transition to renewables, or transition to some arbitrary %), a peak-oil mitigation target, a peak fossil-fuel mitigation target, and a climate change mitigation target.  All have differences and similarities.  Clearly, its one can define a "target" that is plainly achievable, as can one define a "target" that simply can't be done (e.g. 100% transition in 5 years).  As such, the definition of "transition target" represents an easily manipulable variable in any discussion of renewables transition.  If two people or organizations don't recognize the same target, they'll be constantly talking past each other in discussing renewables and the practicality of transition.  While I certainly don't think that I'll be able to convince all parties to adopt a unified transition target in this blog post, I do plan to argue for a threshold target that, in my opinion, represents a minimum rate of transition to keep the "viridian vision" of a renewable future possible:  a peak oil mitigation target.

So, it seems clear that a renewable energy transition will need to, at a minimum, replace the decline in oil production with renewable energy generation.  I'll elaborate on why I draw this line in the sand below, but in brief the viridian vision (by which I mean a general continuation of our current neo-liberal, capitalist/market-socialist civilizational structure into the distant future by leveraging technological advances and a transition to a renewable energy base and "green" economic foundation) requires that we maintain generally the same level of present energy consumption into the foreseeable future.

Why this focus on the "viridian vision"?  I think my personal biases are clear:  I'm very skeptical about the practicality of viridian vision--to be more plain, I don't think it's realistic, and further I think it's the modern opiate of the masses when it comes to confronting current energy issues.  That said, I think anyone who refuses to recognize that both 1) the viridian may be possible, and that 2) it may be fundamentally impossible is taking a faith-based and irrational position.  I don't want anyone to accuse me of hiding the ball as this series progresses--my own studies to date suggest that the renewables transition necessary to fuel the viridian vision is most likely not realistic, and my purpose in this series is to build an argument to this effect.  I'm not trying to be pessimistic.  Rather, I'm trying to prevent a waste of effort, focus, and our limited (and dwindling) supply of surplus energy on an epochal folly.  For lack of a better analogy, it's a bit like our childhood fantasies:  at some point, the little league baseball player needs to give up on the dream of becoming a star professional athlete and focus on a more realistic plan for the future.  Sure, for any given kid it's a possibility to become the next big star, but it would be folly to advise all of them to pursue that dream at all expense.

It's also important to point out the obvious, that there are significant differences between the energy produced by renewable technologies (that, for our purposes, produce electricity) and the energy lost by declining oil production.  In general terms, in order to use the electric energy produced by renewables to replace oil, there will be an additional energy cost required to transition the energy-consuming infrastructure to utilize electricity rather than oil.  This will increase the overall amount of energy required to affect this transition.  For the time being, I'll ignore this additional cost--the result is that my estimates will be more conservative than an estimate that would account for these additional transition demands.

One key argument in favor of the viridian vision is that we can mitigate peak oil with increases in efficiency and energy conservation.  These arguments generally don't, however, address how we're going to meet the energy demands of 1) a growing population, and 2) a huge third-world population that wants to live at Western standards of energy consumption.  The more optimistic population estimates show the Earth's population peaking at 8.3 billion, and more pessimistic estimates show population peaks between 9 and 13 billion.  It's important to point out that may population estimates reason that population will stabilize--and then decline--because of the effect of bringing the standard of living of the world's poor closer to Western standards.  Will the energy pressures presented by population growth and efforts to improve living standards roughly balance out any improvements in efficiency and conservation?  I think so.  In fact, I think that this is overly optimistic, and that demographic pressures will more than eat up any energy savings from efficiency and conservation.  For this reason, I think that we must increase renewable generation capacity at the same rate that oil production declines--we can't count on efficiency and conservation to make up any of this decline.

Additionally, any renewables transition that attempts to mitigate peak oil must cope with the disparity between effective ramp-up rates and effective oil decline rates.  It's nothing more than a simple issue of math:  if you use a post-peak decline rate of 5% for oil decline, then that works out to about 4.4 million barrels per day of decline per year, gradually decreasing over time.  Conversely, because the current renewable generation base is so small (excluding hydropower, which can't be easily ramped up), even a 100% per year increase in renewable generation comes nowhere close to mitigating this 4.4 million barrel per day decline in the early years.  At some point, a 100% annual increase in renewable generation overtakes the declining annual oil production decline figure, but there is a significant gap, especially if we are currently at or very near peak oil.  For this reason, we can't necessarily look at the rate of increase of renewables generation over a 20 or 30 year window, because this long-term view alone may overlook a very significant energy gap.  It's possible that this gap can be filled with fossil alternatives that are not yet at peak--specifically coal and gas--but that's probably the best we can expect from such fossil alternatives given that they are already experiencing significant EROEI declines (and cost increases) and that their climate consequences may be incompatible with the viridian vision...

All of these values--renewable generation, population growth, conservation, efficiency--are arbitrary decisions.  There are simply too many variables to produce a single, agreed set of assumptions on which to base a target estimate.  Here, my goal is simply to make my assumptions (and their rationale) clear so that others can question them and change them if they wish.  Ultimately, I'll continue with this Renewables Hump series using this peak oil mitigation transition target outlined below.  If others have alternative targets, it should be relatively simple to apply the remainder of this series to those different targets...

Numbers:

In looking at these figures, I'm choosing to ignore hydropower, which has a current generation capacity of approximately 800 GW.  My rationale is that hydropower is largely location constrained, and is not scalable in the way that other renewables (especially wind and solar) are.  For example, only about 10 GW of hydropower were added in 2008.  Compare this to a rough doubling in wind generation capacity.

The world consumes roughly  500 Quads per year (Quadrillion BTUs) from all energy sources.  Of this roughly 186 Quads come from oil consumption.  If you accept a post-peak decline rate of 5% per year, then that represents a decline of 9.3 Quads per year.  9.3 Quads equates to roughly 102.3 GW-years, or 896,000 GWh.  To round that off, let's call it 100 GW-years, or 900,000 GW-hours.  That's how much new renewable generation must be added each year going forward.  That's the transition target.  How does that compare with current renewable generation rates? 

The current global installed (nameplate) solar capacity is about 15 GW, including about 5.5 GW added in 2008.  That works out to roughly 1 GW-year of solar generation capacity added in 2008.  One EIA study estimates that, under an "aggressive" growth scenario, total all sources of solar power could displace a total of 22 Quads of fossil fuel consumption by 2050 (that's the total from present to 2050, to an annual rate).  Clearly this rate of transition is woefully insufficient to mitigate peak oil.

At the end of 2008, global (nameplate) wind generation capacity was 121 GW.  That works out to roughly 42 GW-years of total global wind generation, of which 35 GW, or about 12 GW-years of wind generation was added in 2008.  Combining solar and wind, we added about 13 GW-years of renewable generation capacity in 2008.  That's a bit over 10% of the rate at which we'll need to add new renewable capacity each year just to compensate for a 5% global oil production decline rate (not to mention future natural gas decline, coal decline, etc.).  There are two take-aways from this:  1) the current rate at which we are increasing renewable energy generation is an order of magnitude lower than that necessary to mitigate peak oil, and 2) the amount of energy invested in renewable energy projects at present does not pose the kind of energy drain that will be presented by investment sufficient to mitigate peak oil.

On this last point, mitigating a decline of 4.4 million barrels of oil per day each year with new renewable generation capacity will impose a significant up-front energy cost.  If the energy payback time is 1 year for the mitigating renewable source, and this represents a 90% increase in current renewable energy investment, then we need to invest the equivalent of an additional 3.96 million barrels of oil each day to facilitate the transition.  That's like adding another half of China to global demand, and that 1-year payback time assumes an EROEI of 40:1 on a 40-year generating life.  If the energy payback time is 2 years (or a 20:1 EROEI) then you can add another full China to global demand.  If it's 10 years (an EROEi of 4:1), then go ahead and add 5 Chinas.  You can see where this is going--getting an accurate measure of EROEI, and properly understanding the mechanics of scalability, are critical before we can determine if it's possible to mitigate peak oil with renewables...