Proposal for Better Way to Rate Renewable Energy Windmills

May 26, 2017

I’ve always been uncomfortable when I read or hear media reports of the proud completion of yet another windmill far where they seem to always say something like “Will meet the energy needs of more than 60,000 homes” or something like that. How they come up with that number is never explained and I suspect what they do is take the sum of the name-plate power rating capacity for each machine, multiply by some “attractive” load factor (say 30 or 40% where in fact the number is often closer to 20% or below) and call it a day.

I have a proposal for a different and probably more informative way.

Developers of windmill fields surely do short-interval (continuous?) long-duration (a year or two?) time-series site survey measurements of the patterns of winds (speed, direction, vertical profile) in the locations where they plan to plant a machine. It is from this wind from which energy is transferred into useful power to be dispatched to the customer. Using that data with some science and engineering, and the Betz Limit one can surely compute a probability distribution profile of the expected power output of that wind stream passing the windmill. Regardless of the name plate capacity of the machine (and how many near-by homes there are), this probability distribution shows how much power can be expected to come out. Certainly the size of the machine and other machine parameters are part of the output energy computation, but the basic input is how much wind does and will pass through the machine. And remember, no wind–no power. Too much wind–no power.

Then, agree (government can do this sort of thing easily) the probability percentile to pick off the cumulative distribution curve as the “standard” rating for that machine at that location and the time over which power/energy is harvested. I propose we use 95%, which means the windmill operator can say “We have 95% probability that over the course of a year this machine will create produce XX mega-watt-hours of electricity over the course of a year for our customers.” They can compute the confidence factor on that number, but there probably is no need to quote in media, but the investors surely should be informed.

With other methods of power generation, e.g. burning fuel (coal, gas, oil, nuclear, etc.) to produce power based on the thermodynamics capability of the machine it makes sense to quote the power capacity of the machine as the economic basis of how much sellable power will be produced. This is because the machine operators will, for sure, keep feeding the machine with a constant supply of fuel to ensure the machine produces what the customers demand and the investors expect. Whatever it takes, they will do it.

However, this approach to look at the machine to quote power production capability is misleading for renewable energy machines. Yes, serves the interests of those who want more of these machines, but what about the customers and the rest of society?

With renewable energy machines using solar, wind, tides, and waves … the operators are no longer in charge or nor have any responsibility to fuel the plant. We are at the mercy of Mother Nature.

Hence measure and predict Mother Nature.

Will it happen? Probably not. Or maybe they do it now. I don’t know. But if I had anything to do with it we would.

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Internal Waves. Continuing the meme of Everything Affected by Global Warming. Sigh.

March 4, 2014

Last evening I attended an enjoyable presentation on ocean dynamics at the Royal Society of Edinburgh by Professor Peter Davies, Professor of Fluid Dynamics, University of Dundee.

He spoke about “internal gravity waves” which propagate due to water density stratifications. While I was aware of such waves from my days a grad school studying ocean waves, I was not aware of their power as discussed by Professor Davies. He also told us about “dead water”, caused by internal waves, in which a boat may experience strong resistance to forward motion in apparently calm conditions. I was unaware of such a phenomenon.

I was pleased that not once during his presentation he mentioned those two words “global warming”. That is unusual for a presentation at the RSE. It took until the third question during Q&A for someone in the audience to ask if global warming had any affect. Using many words, Professor Davies basically said “no”. That didn’t stop a second follow-up question by someone else who prefixed that question with something about “global warming, which has melted the polar ice caps …”.

At that point I decided to leave. Perhaps I missed the next big risk to civilisation–huge and larger anthropogenic internal ocean waves.


Just How Unusual is UK Weather?

June 19, 2013

The press, television news, and even some people in UK are all aflutter about the “unusual” weather in UK.

My take is that it has been cool but dry, based on my attempts to use my global HQ office in the back garden and the state of our lawn (many brown patches of thirsty grass).

The problem is so big that the UK Met Office convened a conference to discuss why this “unusual” weather is happening. This meeting also received a lot of attention from the press and television news.

Neil Catto has written a guest essay on Watts Up with That about the “unusual” weather at a “southern UK location”. Well worth a read. Sort of what I notice without doing the number crunching.

“… 14.5 years of perfectly normal very stable weather.”

Update: Bishop Hill and “Commentors” comment here.


Quantitative Risk Assessment

June 13, 2013

Yesterday I attended the Palisade Software Risk Conference in London. While one or two of the presentations were a miss, there were some terrific presentations and conversations with other attendees which were enlightening and inspiring. It had been a while since I used @Risk software in earnest so I thought it was about time to kick the tires again.

I’m organising a golf outing for later this summer. We are certain of the costs (unit and overheads), but are uncertain about how many people will attend. I am assuming 21, but it could be as much as 28 (unlikely) or something less than 21. I’ve assumed here 12 as the minimum.

I know how much the golf rounds cost, how much food costs, the budget for the prizes, etc.

We plan to charge £100, which is £15 more than we normally charge. More than that is considered beyond the market.

How does this look if we model it in @Risk? See the following summary of the computation of surplus income:

The spreadsheet:

Capture

The Output (surplus) shown as a probability distribution:

Capture1

Nice. It tells me we should consider charging more for the event as the current projections show we are unlikely to cover our costs.