The Pickering Nuclear Generating Station, on the east edge of Canada’s largest city, Toronto, is a good take-off point for a discussion of the strengths and limitations of Vaclav Smil’s power density framework.
The Pickering complex is one of the older nuclear power plants operating in North America. Brought on line in 1971, the plant includes eight CANDU reactors (two of which are now permanently shut down). The complex also includes a single wind turbine, brought online in 2001.
The CANDU reactors are rated, at full power, at about 3100 Megawatts (MW). The wind turbine, which at 117 meters high was one of North America’s largest when it was installed, is rated at 1.8 MW at full power. (Because the nuclear reactor runs at full power for many more hours in a year, the disparity in actual output is even greater than the above figures suggest.)
How do these figures translate to power density, or power per unit of land?
The Pickering nuclear station stands cheek-by-jowl with other industrial sites and with well-used Lake Ontario waterfront parks. With a small land footprint, its power density is likely towards the high end – 7,600 W/m2 – of the range of nuclear generating stations Smil considers in Power Density. Had it been built with a substantial buffer zone, as is the case with many newer nuclear power plants, the power density might only be half as high.
A nuclear power plant, of course, requires a complex fuel supply chain that starts at a uranium mine. To arrive at more realistic power density estimates, Smil considers a range of mining and processing scenarios. When a nuclear station’s output is prorated over all the land used – land for the plant site itself, plus land for mining, processing and spent fuel storage – Smil estimates a power density of about 500 W/m2 in what he considers the most representative, mid-range of several examples.
The Cameco facility in Port Hope, Ontario processes uranium for nuclear reactors. With no significant buffer around the plant, its land area is small and its power density high. Smil calculates its conversion power density at approximately 100,000 W / square meter, with the plant running at 50% capacity.
And wind turbines? Smil looks at average outputs from a variety of wind farm sites, and arrives at an estimated power density of about 1 W/m2.
So nuclear power has about 500 times the power density of wind turbines? If only it were that simple.
Inside and outside the boundary
In Power Density, Smil takes care to explain the “boundary problem”: defining what is being included or excluded in an analysis. With wind farms, for example, which land area is used in the calculation? Is it just the area of the turbine’s concrete base, or should it be all the land around and between turbines (in the common scenario of a large cluster of turbines spaced throughout a wind farm)? There is no obviously correct answer to this question.
On the one hand, land between turbines can be and often is used as pasture or as crop land. On the other hand, access roads may break up the landscape and make some human uses impractical, as well as reducing the viability of the land for species that require larger uninterrupted spaces. Finally, there is considerable controversy about how close to wind turbines people can safely live, leading to buffer zones of varying sizes around turbine sites. Thus in this case the power output side of the quotient is relatively easy to determine, but the land area is not.
Wind turbines line the horizon in Murray County, Minnesota, 2012.
Smil emphasizes the importance of clearly stating the boundary assumptions used in a particular analysis. For the average wind turbine power density of 1 W/m2, he is including the whole land area of a wind farm.
That approach is useful in giving us a sense of how much area would need to be occupied by wind farms to produce the equivalent power of a single nuclear power plant. The mid-range power station cited above (with overall power density of 500 W/m2) takes up about 1360 hectares in the uranium mining-processing-generating station chain. A wind farm of equivalent total power output would sprawl across 680,000 hectares of land, or 6,800 square kilometers, or a square with 82 km per side.
A wind power evangelist, on the other hand, could argue that the wind farms remain mostly devoted to agriculture, and with the concrete bases of the towers only taking 1% of the wind farm area, the power density should be calculated at 100 instead of 1W/m2.
Similar questions apply in many power density calculations. A hydro transmission corridor takes a broad stripe of countryside, but the area fenced off for the pylons is small. Most land in the corridor may continue to be used for grazing, though many other land uses will be off-limits. So you could use the area of the whole corridor in calculating power density – plus, perhaps, another buffer on each side if you believe that electromagnetic fields near power lines make those areas unsafe for living creatures. Or you could use just the area fenced off directly around the pylons. The respective power densities will vary by orders of magnitude.
If the land area is not simple to quantify when things go right, it is even more difficult when things go wrong. A drilling pad for a fracked shale gas may only be a hectare or two, so during the brief decade or two of the well’s productive life, the power density is quite high. But if fracking water leaks into an aquifer, the gas well may have drastic impacts on a far greater area of land – and that impact may continue even when the fracking boom is history.
The boundary problem is most tangled when resource extraction and consumption effects have uncertain extents in both space and time. As mentioned in the previous installment in this series, sometimes non-renewable energy facilities can be reclaimed for a full range of other uses. But the best-case scenario doesn’t always apply.
In mountain-top removal coal mining, there is a wide area of ecological devastation during the mining. But once the energy extraction drops to 0 and the mining corporation files bankruptcy, how much time will pass before the flattened mountains and filled-in valleys become healthy ecosystems again?
Or take the Pickering Nuclear Generation Station. The plant is scheduled to shut down about 2020, but its operators, Ontario Power Generation, say they will need to allow the interior radioactivity to cool for 15 years before they can begin to dismantle the reactor. By their own estimates the power plant buildings won’t be available for other uses until around 2060. Those placing bets on whether this will all go according to schedule can check back in 45 years.
In the meantime the plant will occupy land but produce no power; should the years of non-production be included in calculating an average power density? If decommissioning fails to make the site safe for a century or more, the overall power density will be paltry indeed.
In summary, Smil’s power density framework helps explain why it has taken high-power-density technologies to fuel our high-energy-consumption society, even for a single century. It helps explain why low power density technologies, such as solar and wind power, will not replace our current energy infrastructure or current demand for decades, if ever.
But the boundary problem is a window on the inherent limitations of the approach. For the past century our energy has appeared cheap and power densities have appeared high. Perhaps the low cost and the high power density are both due, in significant part, to important externalities that were not included in calculations.
Top photo: Pickering Nuclear Generating Station site, including wind turbine, on the shoreline of Lake Ontario near Toronto.
