Renewable Revolution

EROI PART 1 OF 2 PARTS

The purpose of this post is to discuss a term near and dear to the heart of any investor in energy products. That is the term EROI. It is important because we all need to know how cost effective any energy product technology is.

In a sane society, if an energy product is found to have a higher EROI than what is presently popular, subsidized by government or simply enjoys monopoly price control, then it would be a no-brainer that the new energy product should, of course, replace the one with a lower EROI. The natural tendency for energy corporations to try to extract maximum profit by externalizing costs aside for a moment, let's compare EROI on a few energy products and also explain the concept of EROI:

Snippet 1:
 [The most recent summary of work and data on the EROI of fuels was conducted in the summer of 2007 at SUNY ESF and appeared on The Oil Drum website and in a readable summary by Richard Heinberg. This paper summarizes the findings of that study, and also those preceding and subsequent to it where available. It also summarizes issues raised by some concerning the findings of these studies and with the calculations within.]

Snippet 2:
[Oil and conventional natural gas are usually studied together because they often occur in the same fields, have overlapping production operations and data archiving.]

Snippet 3:
[.. authors also estimated through linear extrapolation that the EROI for global oil and conventional natural gas could reach 1:1 as soon as about 2022 given alternative input measurement methods (Figure 2).]

Snippet 4:
[The authors of this EROI study note that they exclude the interest paid on debts to purchase foreign oil. Including that cost presumably would decrease EROI. As can be expected, the EROI of imported oil to the U.S. is mostly a reflection of the price of oil relative to the price of general goods and services at that time       :evil4:(Figure 3).]

The authors note that the differences in EROI can sometimes be attributed to differences in system boundaries and technologies. However, overall there is a lack of empirical information on the subject. ]

Snippet 4
[Wind energy is one of the fastest growing renewable energies in the world today, although it still represents far less than one percent of global or U.S. energy use. Since it is renewable energy, EROI is not calculated the same as for finite resources. The energy cost for such renewable systems is mostly the very large capital cost per unit output and the backup systems needed, for two thirds of the time the wind is not blowing.

As a result, the input for the EROI equation is mostly upfront, and the return over the lifetime of the system—which largely is not known well.

For renewable resources a slightly different type of EROI is often used, the “energy pay back time” (EPBT). EPBT is the time it takes for the system to generate the same amount of energy that went into creating, maintaining, and disposing of it, and so the boundaries used to define the EPBT are those incorporated into the EROI.

Although the SUNY ESF study did not calculate EROI for wind they were able to use a recent “meta-analysis” study by Cleveland and Kubiszewski [27].

In this study the authors examined 112 turbines from 41 analyses of both conceptual and operational nature. The system boundaries included the manufacture of components, transportation of components to the construction site, the construction of the facility itself, operation and maintenance over the lifetime of the facility, overhead, possible grid connection costs, and decommissioning where possible, however not all studies include the same scope of analysis.

The authors concluded that the average EROI for all systems studied is 24.6:1 and that for all operational studies is 18.1:1. The operational studies provide lower EROIs because the simulations run in conceptual models appear to assume conditions to be more favorable than actually experienced on the ground.

The authors found that the EROI tends to increase with the size of the turbine. They conclude that there are three reasons for this. First, that smaller turbines are of older design and can be less efficient, so despite a larger initial capital investment larger systems compensate with larger energy outputs; second that larger models have larger rotor diameters so they can operate at lower wind speeds and capture more wind energy at higher efficiencies year round; and finally because of their size, larger models are taller and can take advantage of the higher wind speeds farther above ground. ]

Snippet 5:
[The use of Solar photovoltaics (PV) are increasing almost as rapidly as wind systems, although they too represent far less than 1 percent of the energy used by the U.S. or the world. Similarly, they are a renewable source of energy and thus the EROIs are also calculated using the same idea. Although there are very few studies which perform “bottom up” analysis of the PV systems we are familiar with today, we can calculate the EROI by dividing the lifetime of a module by its energy payback time (EPBT). Like wind turbines, PV EPBT can vary depending on the location of production and installation. It can also be affected by the materials used to make the modules, and the efficiency with which it operates - especially under extreme temperatures.

The SUNY ESF study looked at a number of life cycle analyses from 2000 to 2008 on a range of PV systems to determine system lifetimes and EPBT, and subsequently calculated EROI [28]. The system lifetimes and EPBT are typically modeled as opposed to empirically measured. As a result, EROI is usually presented as a range. Typically the author found most operational systems to have an EROI of approximately 3–10:1. ]=or> THAN 20:1! 

Snippet 6:
[The SUNY ESF study estimated that one wave energy project could have an EROI of approximately 15:1 [34].  ]

Snippet 7:
[ 13. Discussion
There has been a surprisingly small amount of work done in the field of EROI calculation despite its obvious uses and age. From this review it can be inferred that there are only a handful of people seriously working on the issues related to energy return on investment. As such it does not come as a surprise that the information is scarce and unrefined at best–although perhaps not in the case of ethanol.  :evil4: Additionally there is a great deal of rather misleading material presented in the media and very few with the training to cut through the fog or deliberate lies. We have presented what we believe to be virtually all of the data available until this special issue.

Since the 1980’s the energy information required to make such calculations have become even scarcer, with the possible exception of some European life cycle analyses. This is a terrible state of affairs given the massive changes in our energy situation unfolding daily.

We need to make enormously important decisions but do not have the studies, the data or the trained personnel to do so. Thus we are left principally with poorly informed politicians, industry advocacy and a blind but misguided faith in market solutions to make critical decisions about how to invest our quite limited remaining high quality energy resources. Our major scientific funding agencies such as the National Science Foundation and even the Department of Energy have been criminally negligent by avoiding any serious programs to undertake proper EROI, environmental effects, or other studies, while our federal energy data collections degrade year by year under misguided cost cutting and free market policies.

As stated by Murphy and Hall [15], there needs to be a concerted effort to make energy information more transparent to the people so we can better understand what we are doing and where we are going. Given what we do know, it seems that the EROI of the fuels we depend on most are in decline; whereas the EROI for those fuels we hope to replace them with are lower than we have enjoyed in the past.  This leads one to believe that the current rates of energy consumption per capita we are experiencing are in no way sustainable in the long run. At best, the renewable energies we look toward may only cushion this decline.]

Sustainability 2011, 3, 1796-1809; doi:10.3390/su3101796
 www.mdpi.com/journal/sustainability 
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What does all the above mean to you and me? It means EROI math has great difficulty measuring renewables and, due to the boundary framework established for upstream and downstream costs including the EXCLUSION of environmental costs, has the potential to produce some fairly happy numbers for fossil fuels and nuclear. Yet even by the present computation convention, EROI is headed downwards for fossil fuels and nuclear.
Let's explore EROI some more:
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Snippet 1:
[Measuring the EROEI of a single physical process is unambiguous, but there is no agreed standard on which activities should be included in measuring the EROEI of an economic process. In addition, the form of energy of the input can be completely different from the output.]

Snippet 2:
[How deep should the probing in the supply chain of the tools being used to generate energy go? For example, if steel is being used to drill for oil or construct a nuclear power plant, should the energy input of the steel be taken into account, should the energy input into building the factory being used to construct the steel be taken into account and amortized? Should the energy input of the roads which are used to ferry the goods be taken into account? What about the energy used to cook the steelworker's breakfasts? These are complex questions evading simple answers. A full accounting would require considerations of opportunity costs and comparing total energy expenditures in the presence and absence of this economic activity.]

Snippet 3:
[Conventional economic analysis has no formal accounting rules for the consideration of waste products that are created in the production of the ultimate output. For example, differing economic and energy values placed on the waste products generated in the production of ethanol makes the calculation of this fuel's true EROEI extremely difficult.]

source:
http://en.wikipedia.org/wiki/Energy_returned_on_energy_invested
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And what about environmental degradation costs? Don't they matter in the "real world"? Can we so narrowly define a process like EROI that we deliberately exclude costs that aren't immediately quantifiable? Why are fossil fuel or nuclear energy corporations the first to bray and warn that all new technologies need to have the precautionary principle of science applied to them but get quite huffy when you question EROI numbers for their products? If that's the "real world', then we have a rather serious objectivity deficit in play with EROI math.

Here is an interesting article about a study of algal biocrude EROI. I bring this to your attention because it shows a very serious and responsible approach to determining EROI which I believe is sorely lacking in fossil and nuclear fuels:
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ENERGY RETURN ON INVESTMENT FOR ALGAL BIOCRUDE

Snippet 1:
[Over the last year a student (Colin Beal) at the University of Texas, Austin, has been characterizing the experimental set-up at the Center for Electromechanics for testing an algae to bio-oil process. The process stops short of converting the bio-oil into biodiesel, and he presented the results at a recent conference: Beal, Colin M., Hebner, Robert E., Webber, Michael E., Ruoff, Rodney S., and Seibert, A. Frank. THE ENERGY RETURN ON INVESTMENT FOR ALGAL BIOCRUDE: RESULTS FOR A RESEARCH PRODUCTION FACILITY, Proceedings of the ASME 2010 International Mechanical Engineering Congress & Exposition IMECE2010 November 12–18, 2010, Vancouver, British Columbia, Canada, IMECE2010-38244.]

Snippet 2:
[the stage of development of the entire technology and process of inventing new energy sources and pathways. It is important that we understand how to interpret findings “from the lab” into real-world or industrial-scale processes. To anticipate the future EROI of an algae to biofuel process, Colin performed two extra analyses to anticipate what might be possible if anticipated advances in technology and processing occur: a Reduced Case and Literature Model calculation.]

CONTINUED IN PART 2