energy. Work funded by the US Department of Energy, termed the ‘Hirsch Report’, suggests we need 10–20 years of lead time before a global peak in oil production to prepare alternative infrastructure and new energy systems to avoid dramatic liquid fuel shortfalls (Hirsch 2005). Unfortunately, the alternatives proposed in that report, even were they to be timely – oil shale, tar sands, and coal-to-liquids using Fischer-Tropsch technology – all have deleterious impacts on environmental health (Jaramillo 2008). Development of energy systems from renewable sources such as wind, solar, hydroelectric, are considered an important priority use for our existing high net energy assets. However, the complexity of our daily routines cannot easily be replicated by sources with differing energy properties such as; net energy, gravimetric, volumetric and power densities, intermittency, temporal and spatial distribution, and volatility compared to fossil fuels (Cleveland 2007). Additionally, the recent bonanza of free energy has liberated historical constraints of human impact on the planet, as we have created ‘heat machines’ and infrastructure that magnifies energy’s impact in the pursuit of the modern barometer of relative fitness: economic growth. This pursuit has in turn accelerated impact on the planet’s ecosystems through measures of anthropogenic increases of carbon in the atmosphere, reduced depth of topsoil, water shortages, and myriad other environmental factors (Cleveland, 2007). Water is already a limiting resource in many contexts (Gleick 2000), and increasing human withdrawals will have a dramatic effect on the earth’s ecosystems and biodiversity (Alcamo 2005). As such, energy supply choices will have to be studied not only for the energy properties they exhibit, but also on their external impacts.
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of water and energy, that are still ignored. In particular, costs associated with environmental externalities are generally not accounted for (3). Technology Key Specifications Water Use (l/MJ)1 EROWI (MJ/l)2 EROEI Net EROWI2 Direct2 Indirect3 Nuclear Electric - Once-through cooling - National average 33.25 (0.145) NA 0.030 (6.897) 10 0.027 (6.21) Nuclear Electric - Recirculating - National average 1.162 (0.659) NA 0.861 (1.517) 10 0.775 (1.37) Coal Electric - Once-through, sub-critical - National average 28.62 (0.146) NA 0.0349 (6.849) NA NA Coal Electric - Recirculating, sub-critical - National average 0.560 (0.488) NA 1.786 (2.049) NA NA Coal Electric - Cooling pond, sub-critical - National average 18.922 (0.849) NA 0.0528 (1.178) NA NA Tar Sands - Steam Assisted Gravity Drainage - in situ – Alberta, Canada (0.061– 0.122) NA (16.39 – 8.19) 3.75 (12.02 – 6.01) Biomass Electric - 113 MW Biomass IGCC – US - Non-irrigated hybrid poplar 0.238 0.021 3.86 15.6 3.61 Biomass Electric - IGCC - Irrigated hybrid poplar – Italy 0.238 3.85 0.245 1.60 0.092 Biomass Electric - IGCC with various feedstocks -Irrigated at a rate of 400 L/kg dry biomass. 40 NA 0.025 5.0 0.02
Investment cost per annum: 135.1 million US$ Operations cost per annum: 89.7 million US$ Total cost per annum: 224.8 million US$ Total cost per kWh of nuclear output: 1.1 ct Handicap (over 8ct base cost/kWh): 13.7% Table 13 – Costs of gap matching with replacement energy technologies In the summary below, it becomes evident that the handicaps evaluated from this methodology are remarkably similar to the ones obtained using methodology 1. Wind would – based on Denmark West data, receive a handicap of 71.2% (vs. 70.5% in method 1), nuclear a handicap of 23.6% (vs. 19.8 from method 1) Type of fuel and nameplate capacity Total consumption Oversupply Oversupply handicap Largest supply gap Cost of supply gap in % of base technology cost Total EROI Handicap Nuclear (~2.35 GWe) 20’550 GWh 2’038 GWh 9.9% 1.33 GW 13.7% 23.6% Wind DK West (~10 GWe) 20’550 GWh 7’243 GWh 35.2% 3.5 GW 36.0% 71.2% Wind Spain (~145 GWe) 251’630 GWh 62’102 GWh 24.6% 38.1 GW 32.0% 56.6% Table 14 – Gap matching EROI handicaps From the above application of method 2, despite the relative inaccuracy of a cost-based comparision, it becomes obvious that results deviate only slightly from those obtained in a strictly mathematical fashion in method 1.
reference –total generation capacity in the entire European UCTE area was 800 GWe in 2007. (). Given the above results, and a number of unsuccessful attempts at modeling outputs of much higher capacity expecting to eliminate those large gaps, method 3 was abandoned. 6.6 The Sharpe Ratio of Energy Given that I outlined portfolio metrics for finance, the paper needs to have a ‘portfolio’ section here, showing coal, nuclear, wind, solar in relation to the energy equivalent of a ‘treasury bond’ (this will be natural gas). I have just gotten data for Spain on an hourly for 2009 and will create this matrix before my defense. 6.6 Discussion When accounting for risks in delivering energy technologies, methods 1 and 2 delivered highly comparable results despite the relative uncertainty regarding the cost model used in method 2. It thus seems appropriate to introduce handicaps according the deviations between supply and demand in flow-based output systems such as electricity grids. Technology EROI (undiscounted) Method 1 Method 2 Wind 19.2 5.7 – 9.7 5.5 – 8.3 Nuclear 15 12 11.5 Table 17 – Risk-adjusted EROI for Wind and Nuclear As we have attempted to demonstrate here, developing a metric for energy flows seems feasible and important. It remains an open question how to develop a similar methodology for stock based energy systems. Due to fungibility and
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