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Mackay's Musings

Posted: 1/09/2010 10:25:40 AM

Engineering statistics and lies and a chapter on wind energy

Dear Colleagues

Windpower is shaping up as the main source of renewable power and the key competitor to fossil fuels; although apparently 85% of wind projects overestimated their production. As we all know with the uncertainty of wind, there are problems with getting the power at the right time. And storage of power generated when the wind is really pumping is still a tricky area. So this is where an entirely new field of statistics is developing – this is a vital part of generating wind power that you need to understand as an engineering professional – no matter whether you are  a tradesman or engineer – as wind power is going to be a key technology in the short term future. Wind is horribly variable - unlike the ‘good old’ coal powered power stations where you simply had to get the supply of fossil fuels right; shovel it into the furnaces, generate the power and you were home and hosed.

For those of you interested in the topic of the fundamentals of wind power, there is a 30 p. chapter download at the end of this blog, from one our renewable energy manuals to refresh your knowledge.

The European Union has committed that 20% of the energy generated by 2020 will be renewable and mainly through wind power. America has a similar target. Unhappily capacity in windpower does not actually mean the same thing as delivery of electricity. And this is where there is growing activity in wind forecasting. An entirely new engineering industry seeking to provide tight predictions.

Statistics forms a key part in doing the necessary forecasting. This is to achieve two objectives: long term statistics to get the necessary financing to build your wind farm and then once you are operating, you need to be able to forecast short term specifics of provision of power.

Bankers inevitably are not going to loan you money for your windpower project unless there are solid estimates of the wind farm’s capacity. One of the key techniques used is ‘measure, correlate and predict’ analyses. This involves measuring the wind at your proposed site for a year or so (two years would be great); correlating it with historical wind data from a nearby weather station; and then building a statistical model of the potential wind resources. Bear in mind that output from a windfarm can vary by 20% from year to year.

As you well know - wind is extraordinarily sensitive to the shape of the landscape; so there may no correlation between the wind at the reference site (the weather station which wasn’t designed to measure the vagaries of wind speed) and your proposed wind farm site. As wind turbines are much taller than measurement towers, you will often find significant differences in the strength of the wind at different altitudes. Once you have estimates of wind, you apply it to the power curve of the wind turbine you select. Power output is proportional to the cube of wind speed; so small fluctuations can result in huge changes to the energy output. You also need to estimate wake losses, which are the losses associated with an upwind turbine reducing the wind available to the down wind turbine.

Banks will lend money based on the so-called ‘P90’ wind value – this is the average wind speed in which they can be 90% confident. The closer the P90 reading is to the measured average speed (no greater than a 15% variation), the more attractive the site is to investors.

Once your wind farm is up and running, intermittency of wind is a serious issue. For example, in Denmark (20% of its electricity now sourced from wind); a change in the wind speed of 1m/s translates into a huge 450MW in national power output. The overall trick (as it were) is to make wind farms appear as close as possible to power stations in their provision of reliable power at particular times and this is where short term statistical forecasting is key.

Simple forecasting is called ‘persistence forecasting’; which assumes that the wind speed in an hour’s time is the same as now. This is the benchmark approach; but obviously not as accurate as some of the more sophisticated approaches using numerical weather prediction which model the atmosphere as a three dimensional grid, with cells of few kms on each side and grabbing physical data such as pressure, temperature and humidity from sensors. Accurate short term predictions are critical to wind farm operators – the difference between large profits and huge fines for non-compliance.

So next time; you look askance at statistics; realize that they are increasingly a key component in wind power engineering these days. But from an engineering professional point of view, it is certainly worth getting more familiar with how statistics can help you in your day-to-day work.

To quote from a former British prime minister, Benjamin Disraeli:
There are three kinds of lies: lies, damned lies, and statistics.

 
Hopefully, you use statistics in such a way, that this statement is not true.
Download your chapter on wind energy here: http://www.idc-online.com/downloads/renewable_energy_extract.pdf

Thanks to the Economist for an intriguing article on the subject and biofuels.coop/windblog for some interesting stats.

Yours in engineering learning

Steve

Posted: 1/09/2010 10:23:43 AM

W(h)att's up, 'o Engineering professional, when you save energy ?

Dear Colleagues

Most people tend to underestimate or misunderstand energy savings – according to the latest research that is. We tend to focus on insignificant savings such as upgrading light bulbs and twiddling thermostats. Most people grasp the broad and basic issues about energy savings; but they are decidedly unsure about the details, especially when estimating. Apparently participants in the research underestimated both energy use and savings by almost a factor of 3. They also tended to grossly underestimate the massive energy savings that could come from tweaking larger machines such as heaters and clothes dryers. Most people tend to focus on small savings such as switching off lights and ignored (as a typical example) the greater savings from switching their washing machines from hot to warm settings which saves 4kWh for each load of laundry.

It would appear that human psychology causes us to adopt a familiar yardstick (such as the familiar electric light bulb) and then to use this as a benchmark to make predictions. The estimates of savings then tend to cluster around this yard stick (psychologists call this process ‘anchoring’). As a result we tend to grossly underestimate the savings that could be made. Naturally, if the average person used a larger yardstick (beyond the light bulb) the problem may be less pervasive. And if we are good at maths (or arithmetic), we are likely to have a considerably lower level of error.

Based on this, there is probably a case for idiot proof energy saving devices that indicate exactly how much energy we consume.

What can be done?

• Expect people with a limited background in maths to underestimate energy savings.
• Encourage people to focus on the opportunities to squeeze tiny percentage savings from larger machines, resulting in significant savings, rather than focusing on the smaller items and smaller savings.
• Guard against the ‘anchoring effect” when estimating - practiced unintentionally either by yourself or others.
• Naturally, I am not knocking looking at the small things when undertaking energy savings, but merely pointing out the need to also focus on (often quite simply) the ‘bigger fruit’.

Thanks to the National Academy of Sciences (and the Economist) for an interesting piece of research.

Never overestimate what others do. As Cory Doctorow said “Engineers are all basically high-functioning autistics who have no idea how normal people do stuff.”

Regards

Steve