How Does Your Wind Farm Grow?

Calculating what the global saturation point for wind energy would be and if we can generate enough wind power to power half the globe.

WHO: Mark Z. Jacobson (Department of Civil and Environmental Engineering, Stanford University, Stanford, CA)
Cristina L. Archer (College of Earth, Ocean, and Environment, University of Delaware, Newark, DE)

WHAT: Predicting the effectiveness of scaling up wind power to provide half the world’s power requirements by 2030.

WHEN:  September 25 2012 PNAS, Vol 109, No. 39

WHERE: Proceedings of the National Academy of Sciences of the United States of America

TITLE: Saturation wind power potential and its implications for wind energy

I learnt about a new law today; Betz’s Law. Betz was a guy who decided to calculate exactly how much energy could be extracted from the wind by a turbine at any given time mathematically (as you do). He worked out that no turbine can take any more than 59.3% of the energy from the wind. To be able to conceptualise this, you have to think about wind like a physicist. The first law of thermodynamics states that you can’t create or destroy energy; you can only convert it to different forms. Therefore, all wind is just energy in a certain form, and in any system there is a point where the transformation is most efficient and beyond there it takes a lot of effort to get any more energy from the system.

There’s a really cool project being done in the US, where a website has taken data from the National Digital Forecast Database and created a visual representation of what wind would look like if you could see it move. It’s strikingly beautiful, and looks a lot like a Van Gogh painting.

Wind Map by Fernanda Viegas and Martin Wattenberg of hint.fm

The question this paper looks at is: since there is a limit to the amount of energy you can take from a turbine, what is the maximum wind power that can be extracted from a geographical area? They called it the ‘Saturation Wind Power Potential’.

They came up with some interesting findings, as well as probably having a lot of fun along the way because they used 3D Models to do it (I’m telling you, my chemistry molecular model kit was much more like playing with Lego than actual ‘science’). They got into the detail and calculated the potential wind power at 10m off the ground, 100m off the ground (the standard height of a wind turbine) and 10km off the ground in the jet stream.

They then looked at whether it would be possible to scale up wind power globally to meet 50% of the world’s power needs by 2030. Actually measuring the wind power potential for more than 1 Terrawatt (TW) of energy is not possible as there isn’t enough wind power installed yet. But they did mathematically work out that we would need 4million 5 Megawatt (MW) turbines to supply half of the world’s electricity needs in 2030 (5.75TW).

They did four simulations with different turbine densities, because how close together wind turbines are affects their ability to produce power. Put them too close together and they start stealing their neighbour’s wind power. Overall, up to 715TW, the increased number of turbines increases the amount of power in a linear straight line. Once you get above that it slows down and flattens out – once again you need to put much more effort in to get power out.

Predicted wind power saturation potential (from paper)
Grey line – global wind power potential, black line – wind power potential on land only

The saturation point, where no matter how many more turbines you add, they’ll just be stealing energy from each other and not adding anything to the total, was 2,870TW of power globally. Interestingly, they found the wind power available in the jet stream (10km above the ground) was 150% greater than the wind power available 100m above the ground.

There were also some big changes to the results depending on the density. If we placed 4million 5MW turbines and packed them in at 11.3 Watts per m2 (W/m2), they would be too close together and the collected power wouldn’t match the target for half the world’s power by 2030. If you spread them out to 5.6W/m2 the output is still too low. However, once you’ve got them spaced at 2.9W/m2, they produce enough power to meet the required demand.

4million turbines meet demand when they’re 2.9W/m2 apart or further (from paper)

So it turns out wind turbines don’t like it when you cramp their style. But, you can pack them in a bit tighter, only if you then have enough space between your wind farm and your neighbour’s wind farm. It’s a bit like playing wind farm Tetris.

What does this mean though? It means that we can ramp up world wind power production to levels that will meet half our power needs in 2030, which can be integrated with hydro, solar and other renewables with smart grids to power our cities and lifestyles without burning fossil fuels. But it also means we need to think about where we are putting wind farms and how much space they need to be as efficient as possible. We need that renewable energy, so we can’t cramp the wind turbines’ style!

Improved Drought Prediction: Now With Six Soil Layers

Predicting the severity of drought using multiple indices

WHO: Liu Sun, Scott W. Mitchell (Department of Geography and Environmental Studies, Geomatics and Landscape Ecology Research Laboratory, Carleton University, Ottawa, Ontario, Canada)
Andrew Davidson (Department of Geography and Environmental Studies, Carleton University, Ottawa National Land and Water Information Service, Agriculture and Agri-Food Canada, Ottawa, Ontario)

WHAT: Improving the accuracy of drought prediction in the Canadian prairies

WHEN: September 2012

WHERE: International Journal of Climatology, Vol 32, Issue 11, September 2012

TITLE: Multiple drought indices for agricultural drought risk assessment on the Canadian prairies (subs req)

Are you tired of your drought prediction methods using only two layers of soil structure to track moisture? Sick of having to work with constants when you’d much rather be using dynamically calculated values? Well, this paper is for you.

Drought is going to be a big issue with climate change as rainfall patterns change and move. Agricultural yields are not able to increase as quickly as the world’s population increases but people still need to eat.

Drought is going to affect all of us as extreme weather increases from climate change, whether it’s through increased food prices (I’m still upset about bacon), local water restrictions (stop hosing down concrete – stop it now), local ecosystems being stressed or climate refugees from newly arid areas. This is one of the great ironies about climate change – you can’t negotiate with or spin physics. The laws of physics aren’t going to change because of some slick advertising campaign trying to prop up a floundering status-quo, and climate change isn’t going to avoid you if you ignore it.

In terms of drought modelling and prediction, each method currently used has slightly different ways of predicting drought, which means they can’t easily be compared. The method the researchers used for this paper was to modify the original Palmer Drought Severity Index to include more variable data. They accounted for six soil layers and a new evaporation calculation. Instead of using constant numbers for the characteristics of the climate, they allowed each of those to be calculated too. This means most people end up with a giant math headache from extra calculations, but by allowing for greater variability, they also allowed for greater sensitivity and accuracy in their model. The new model was also tested for accuracy against the Palmer Drought Severity Index, Standardised Precipitation Data and Palmer Moisture Anomaly Index methods.

For any of these models to work, they need approximately 30 years of monthly weather data (temperature, rain etc). This paper looked at 1976 – 2003 as it was the period of most consistent data in the area they were studying (the Canadian Prairies).

Then they got into the serious math using all kinds of things like a ‘thin plate smooth spline surface fitting method’ to remove the noise from the data and a linear regression to remove yield differences from better agricultural practices, allowing them to just look at the data that was climate affected.

The different models: red dot indicating the new model. Spot on for most, slightly under for some (from paper)

It went pretty well; their predictions were more accurate than the other standard drought prediction methods, except for predicting extreme drought, which their model under-predicted. This is possibly because there wasn’t a lot of data points in the previous 30 years with extreme drought, so as extreme weather becomes more normal under climate change, their model will probably get more accurate. They also found that the model is more accurate for arid locations, as flooding messes up the model.

As the extreme, unpredictable realities of climate change start to affect everyone in the next decade or so, this drought prediction model will likely be very useful. Predicting the extremes as best we can is going to become an essential tool for preventing massive crop failures as well as loss of human lives.

Renewable Hybrid Systems: Optimising Power Grids

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WHO: Robert Huva, Roger Dargaville and Simon Caine

WHAT: Electrical power grids powered by renewable energy

WHEN: Published in Energy [41 (2012) 326-334]. April 2012

WHERE:  Earth Sciences department, The University of Melbourne, Melbourne, Australia

TITLE:  Prototype large-scale renewable energy system optimisation for Victoria, Australia (subs required)

One of the major barriers to the full scale take-up of renewable energy to power electricity grids has been the need to provide baseload power to users. This is the power required to keep your fridge running through the night, the power to keep traffic lights running all day and night and many other things. It’s the minimum amount of electricity required to keep the modern world running.

Renewable power is not constant, because the sun doesn’t shine at night and it’s not always windy, and water runs through rivers at different speeds depending on the time of year. So in order to provide the constant power needed, a hybrid system of renewable energy sources needs to be used.

This paper from the University of Melbourne in Australia has done that. They used detailed weather maps for the state of Victoria to determine the best locations for solar and wind power.

Victoria, Australia (Google maps)

Best locations for wind (blue) and solar (red)

They then combined the outputs of the solar and the wind with other forms of renewable energy, including hydro-electricity (running water spinning a turbine to make power) and wind-hydro hybrids where excess wind power will pump water up a hill to a raised dam, and when the wind dies down, the dam gets opened and the hydro starts producing electricity.

They found that the entire electricity needs of the state of Victoria could be met from renewable power sources with only 2% back up from natural gas needed.

 Hybrid renewable systems – meeting demand

So what does this mean for reducing the effects of climate change?

It means that renewable power is viable in the state of Victoria, which will allow the state to switch from it’s current power source of brown coal (which is much dirtier than your standard black coal when it burns, releasing more carbon pollution into the atmosphere).

Making the transition to a hybrid renewable system will also significantly reduce carbon emissions in the state of Victoria since 49% of energy in the state comes from coal power. It will create a large number of new jobs, as the renewable energy market increases from 12% (in 2011) to the 98% that has been shown in the research, which we will need to do in the next 30 years if we want to avoid catastrophic climate change.

How can it be done? By ensuring areas are able to access either localised power production (in rural or remote areas), or smart grids (in cities) that are able to monitor and respond to changing power production levels and changing energy use levels, hybrid systems of renewable electricity are fully capable of providing the power we need to run our lives.

*Full disclosure: The name is not a coincidence – this research was conducted by my brother as part of his PhD research (yes, I’m using my brother’s research to test out my own blog 🙂 )