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Wednesday 30 September 2015

Doris - A thought experiment in progress (12) - And your point is?

Doris is a thought experiment running on a Raspberry Pi and a laptop which is intended to explore sustainable energy, an evolving description and discussion can by found in a previous posts starting with:
It is becoming generally accepted that energy storage can increase the proportion of energy generated from sustainable sources.  Regardless of my efforts with Doris, the aspiration supports international conferences and significant investments in technology are being made.  The concept is not futuristic, products such as Tesla's Power-Wall are coming to market and the internet-of-things which can provide data and control functions is evolving.  The question is where does it fit into the energy economy.  I'm an enthusiast for LED lighting, it works, I get payback (albeit on a small investment) and as we replace CFLs, our energy consumption is slowly falling.  For me as an energy consumer, the economics of storage don't work at present.

This is a well worn quote from the CEO of a cosmetics company; "In the factory we make chemicals and in the shop we sell dreams".  Similarly, we don't buy energy, we buy what it facilitates, e.g. lighting, cooking, entertainment etc.  Putting storage into the system does not cause us to use less energy, just gives us the option of increasing the diversity of sources.  Most consumers don't want a hike in their bills, but many might except higher unit costs if the total bill remained unchanged, thus a prerequisite to the adoption of storage might be energy management and efficiency which creates a cash flow for investment in storage.

At an industry level, the sustainable energy generation capacity is increasing, largely due to offshore wind farms.  However, wind farms are underpinned by conventional generating capacity.  The graph shows a breakdown of the sources of electricity on a pair of Saturday afternoons in October, one was a windy day and the other a calm one:

On the calm day, the gas and coal take over from the wind. This is a reasonably simple investment situation based on producing a product and selling it.  Having read the accounts of some generating companies, the owners of some gas fueled plant view  wind as a competitor and a complicating factor in their economics.  From a sustainability perspective (using number picked from thin air) it better to have four gas fueled plants and two wind farm rather than five gas fueled ones and one wind farm.  Storage helps achieve this.  However, the process of investing in storage rather than generating capacity is a complex one.

In the post war period until the 1990s electricity generation was managed by the CEGB.  In recent years the ownership of generating capacity has become highly diverse.  Participants include banks (possibly because of their deep understanding of markets), privatized power stations and interestingly Scandinavian companies with experience of offshore oil and gas operations have taken a position in the UK offshore wind sector. Would a larger element of central planning give a better outcome for emissions and sustainability?



Friday 18 September 2015

Doris - A thought experiment in progress (11) - Flights of Fantasy


Doris is a thought experiment running on a Raspberry Pi and a laptop which is intended to explore sustainable energy, an evolving description and discussion can by found in a previous post:
It is important to remember that Doris is a computer simulation with some arbitrarily set parameters and rules, it exists only in the imagination and has no physical reality.

With a database and software background, it seemed that a good way to learn about wind and solar energy was to find a bunch of varied data sets and poke around them with SQL.  This has been instructive, but so to has gardening and looking at the location of old corn grinding wind mills.

Weather balloons are a source of wind speed data.  I'm guessing, but there is probably a GPS built into the instrumentation which provides the data needed to estimate the balloon's speed and direction.

Wind is fluid flow over a surface, for smooth surfaces like a calm sea, the friction is low compared to that created by a jagged urban environment. The effect of friction is greatest close to the surface, but at around 1,000 meters, it is much less significant.  I think any investment in wind power technology should be preceded by a site survey, but without that it can be useful to attempt to estimate the wind speed from a reference location, one way of correcting for height is this formula:

There a alternatives which give different results, but this one has the virtue of simplicity.  Most surface wind speed data is collected at 10 meters, the graph shows how the wind speed might increase with height, at 1,000 meters, it is almost twice as fast as at 10 meters.


This graph shows a wind speed distribution at 850 meters (a standard reporting level) which was compiled from weather balloon data.  The average speed is approximately 10.0 m/s, at the surface, the average speed in a similar location might be 5.0 m/s.

This data suggests that the sky is a good place to capture the wind's energy. There are some talented and created people who are attempting to do this; there are a couple of links at the bottom of the page which might be a good start to reading around the subject, one of them, the Makani project is backed by Google.  The designs seem to divide into two groups, one approach is to get the generator aloft with  a kite or ballon and feed the electric energy back to earth with a cable attached to the tether.  The other is to capture the kinetic energy with devices like kites or rotors and use this to drive a generator on the ground.

I have not run any upper air data through the Doris simulation software as one of the parameters of the project is that it should only model readily available product and services.  It's easy to find reasons for dismissing airborne wind energy devices but fuel cells used to be exotic but now the technology is being incorporated into production automotive vehicles, so someone might make it work commercially.

Related material

Wednesday 16 September 2015

Doris - A thought experiment in progress (10) - Urban Wind

Doris is a thought experiment running on a Raspberry Pi and a laptop which is intended to explore sustainable energy, an evolving description and discussion can by found in a previous posts:
It is important to remember that Doris is a computer simulation with some arbitrarily set parameters and rules, it exists only in the imagination and has no physical reality.

Back in 2007, before the financial crisis, some DIY superstores were offering small wind turbines.  From what I remember, these had a nameplate rating around 1 kw (at 10 - 12 m/s, or 20 to 25 mph) and cost about £1,500 excluding mounting.  They could be installed either on a pole (10m high (?)) or strapped to a chimney stack (not a good idea).  Stories appeared in the press saying that they did not work too well and the take up was low.

There are several ways of working out the economics of a project, one of which is to watch how people behave, in the 1 km radius of where I live and walk my dog, I am not aware of any households with wind turbines, however, there are a lot with solar panels, mostly PV, but some thermal.  Solar panels will do something useful in any reasonably open south facing location, but turbines are very sensitive to location.

When I first started messing with this stuff, it seemed that there was a lot of data to play with, however, this is a lot of good quality information about the wind around airports, but but not much about urban and rural areas.  I have amused builders working nearby by standing in my backyard with a wind speed meter showing light airs when there is a gale blowing on the seafront a mile to the south.  Whilst trying to understand the variation in wind speed, I did a couple of cycle rides with a wind speed meter.

One of these was around the town, pausing to estimated the average speed over a 3 minute interval in a several locations, public parks, the beach, the cliff above the beach and multi-story car parks, these estimates were then compared to the speed reported from an airfield a few km to the west which was about 16 knots.  The result was this graph:
With exception one of the beach locations, the average speed was significantly less than at the airfield, equally significant was that nature of the wind which was often turbulent and gusty.

Another trip was up to the hills to the north of the town, I used the GPS on my phone to record the location of readings, which were then plotted on a contour map generated using data from the SRTM Space Shuttle mission and overlaid on Google Earth, the result was this map:


The figures are the ratio of the observed speed to that reported at the airfield.  This can be summarized as the wind speed is higher on the ridges than in the valleys, maybe a space rocket was not needed for that observation.

The accuracy of the measurements displayed on both these graphics is not high, but I think they give a reasonable impression.

I'm not convinced that small wind turbines are suitable for dense urban housing, I have memories of the 1987 hurricane that passed through the south of England when the air was full of tiles, trees and other debris, bits of wind turbine would not have been a welcome addition.  However, I am intrigued by some vertical axis designs that could be incorporated into a suitably strengthened roof.  A rotating machine strapped to a Victorian chimney is something no man can face with equanimity.

Running a data set of urban wind through Doris gave this graph.


For this run, the wind generating capacity was reduced to 0.5 kw.  The contribution for wind is significantly less than that of solar and does little to offset the seasonal nature of solar.  The data set was randomly selected and I'm guessing that there could be a wide variation in results, for example, a house located at the top of a ridge, might get a significant yield from a small turbine, whilst one in a sheltered valley would get almost nothing.





Sunday 13 September 2015

Doris - A thought experiment in progress (9) - Going offshore

Doris is a thought experiment running on a Raspberry Pi and a laptop which is intended to explore sustainable energy, an evolving description and discussion can by found in a previous posts:
It is important to remember that Doris is a computer simulation with some arbitrarily set parameters and rules, it exists only in the imagination and has no physical reality.

When I first started looking at wind as a resource, It became apparent that there is a considerable variation in the nature of wind, even within a small area.  For example where I live on the south coast of England, there are at least five regimes (the average wind speeds are for comparative purposes only):
  • The open spaces around an airfield, typically these are located in uncluttered areas away from hills and other natural obstruction to air flow.  Airfields are the largest source of wind speed data, but it may not always be relevant to wind turbines, the average speed might be 5.0 m/s.
  • Urban and wooded areas where the rough surface attenuates the wind speed and causes turbulence, mounting a turbine on a tall mast reduces the effect of these, but these may not be practical or desirable.  In this situation, the average wind speed might be 1.0 - 3.0 m/s.
  • Special locations, these include ridges and hills where the air flow is not obstructed by the surrounding terrain and may even be enhanced by it.  An average speed might be 6.0 m/s, data on these locations is hard to come by, but looking at the location of corn grinding wind mills can be instructive.
  • Offshore, relative to the land, the surface of the sea is smooth and losses to friction and turbulence are much lower, the average wind speed in an offshore location might be 7 - 9 m/s.  Data sources are offshore platforms and moored buoys.
  • Coastal areas.  When the wind is blowing off the sea it can be smooth, but when it is blowing off the land, it becomes turbulent.
The graphs below are not strictly comparable and only serve to show the difference in the nature of offshore and offshore wind.  Doris uses the latest available weather report, to form a distribution, it is preferable to have regular sampling, say, just use the reports that are on the hour.  The height at which the observations was made was probably different.  Height correction was not applied in the compilation of these graphs, although Doris does make some adjustment in estimating the energy that can be extracted from the wind.

For the onshore location, the mean wind speed is approximately 5 m/s and the distribution has a clearly defined mode at around 8 - 10 m/s.
 Offshore, the average wind speed rises to approx. 9 m/s, more importantly, the proportion of observations which are less than 5 m/s is much lower.
The amount of energy that can be extracted from a stream of wind is proportional to the cube of it's velocity, the wind flowing at 7.5 m/s has the potential to provide more than 3 times the energy of one flowing at 5.0 m/s.  This accentuates the difference between onshore and offshore wind.

Whilst the energy yield from offshore wind is potentially greater than onshore, so are the costs which might be two to four times higher.  The higher costs come from two sources, the first is the high cost of working offshore which requires similar equipment to that used by the oil and gas industry for building platforms.  Secondly, the installations need to have the resilience to withstand storm conditions and be tolerant of the corrosive effect of salt.  The relationship between onshore and offshore locations involves some complex economics.

Running an offshore data set through Doris produced the following graph (some software changes are needed to make the solar component in each graph comparable, they will be updated when this is done, however, the nature of the graph is not expected to change much).  To take account of the offshore data set, it was assumed that the access to wind generating capacity was reduced to 0.5 kw, it is 1.0 kw in the onshore base configuration.


The difference between the two plots is the lower draw down from conventional sources, this is in part due to the higher output and in part due to the smaller number of days when the yield from wind is low.


As with all the output from Doris, there are some gross oversimplifications, one of which may be the ability of wind farms to deliver energy at low levels, this needs to be understood and some allowance built into the model.

Author's Note

Due to some odd career advice, I left school at 15 and was for two year's England's most incompetent merchant seaman.  The fact that I have spent most of my working life writing computer software suggests I was not cut out for a life on the ocean wave. At the time I did not know that what I saw on lookout duty on the monkey bridge could be described in terms of wind speed distributions or Fourier transforms of wave motion.  In the context of this post, I appreciate the complexities of offshore operations and that offshore structures must be designed for extreme conditions, not average ones.


Friday 11 September 2015

Doris - A thought experiment in progress (8) - The winter problem

Doris is a thought experiment running on a Raspberry Pi and a laptop which is intended to explore sustainable energy, an evolving description and discussion can by found in a previous posts:
It is important to remember that Doris is a computer simulation with some arbitrarily set parameters and rules, it exists only in the imagination and has no physical reality.

In most energy economies without a significant amount of storage, wind and solar generators are alternatives to those fueled by gas, coal etc.  When the sun does not shine and the wind does not blow (as sometimes happens on a winter's night in December or January), the sustainable sources drop out and gas turbine stations kick in.  Whilst this is not explicitly stated in energy policies, there is a in effect a duplication of generating capacities.  This causes a variety of problems which include grid management where both demand and supply are related to the weather and economic ones.   Investors in conventional generating capacity may not want to participate in a back-up system to a wind farm, they probably want a free standing investment.

Energy policies which attempt to have a degree of sustainability need to take account of the winter problem,  Typically, this the time of year when the demand for energy peaks.

Storage at the household and some grid management at the neighborhood level might make a contribution.  The graph below shows the daily demand for electricity as modeled in Doris, this is roughly 6.8 kwh/day scaled up to four household, the demand has peaks at the start of the waking day and in the evening.  Say, each household has 10 kwh of storage, it could "download" its energy for the day over a six hour period by imposing a load of just over 1 kw on the grid.  Resorting to the usual gross over simplification, if each household does this in turn, the demand is constant over the day and approximately 60% of the level that would be needed if storage was not present in the system.

The upside of this scheme is that the amount of conventional capacity required is reduced and that the efficiency of most plant increases if it can be run at constant load rather than spinning up to take account of a period of light airs or a surge in the demand for hot water for tea making or spinning down when the sun comes out.  Maybe, there is the need for some creativity in ways to channel investment in generating capacity into storage and energy management, this is a challenge but something worth evaluation.

A variation of this theme is the problem caused by solar eclipses.  A recent eclipse during which the sun was obscured briefly over parts of Germany, which has significant solar generating capacity required some planning to avoid grid problems resulting from a sudden loss of power followed shortly after by a rapid surge.

Load management within the household could help reduce the peaks and troughs.  From a limited and unscientific survey, most of the potential relates to cleaning operations.  In many homes, the daily routine is based on meal times and these are more or less fixed.  Devices like the washing machine and the vacuum cleaner can be operated at times which harmonize with the availability of energy, however, this does require some planning.  I am intrigued by the concept of a robotic vacuum cleaner which comes out of its hutch when the solar panels are producing a surplus of energy and sucks up the filth from the living room.

I'm no advocate for returning to the living standards of our grandparents generation when running a household involved humping coal and ash, washing and cleaning were hard, monotonous work.  But there was an appreciation of the seasons and the weather, if possible washing would be done on a "good drying day".



Wednesday 9 September 2015

Doris - A thought experiment in progress (7) - Climate

Doris is a computer simulation designed to explore the use of sustainable energy by a typical household, it has no physical reality.

The post which describes the background to the project, also contains links to related posts.
The core functionality of Doris is the facility to make estimates of the output of wind and solar generators using aviation weather reports (Metars).  It is written in Python 2.7 and accesses historic weather data stored in an SqLite3 database.  The code is modified explore a range of configurations and scenarios, in this case climate.

When I first started seeking out data on wind and solar resources, it became apparent that  it was difficult to make like-for-like comparisons, this was largely due to variations in climate and terrain.  It's easy to look for an explanation in maths and stats, but a quick look through the travel section of a newspaper will provide an answer.  Many North Europeans take their holidays in Spain because of the clear skies and sunshine and few South Europeans trek north to in mid winter because of the wind and cloudy skies.  For wind turbines terrain is important, the ideal location for a turbine is on a ridge which is at right angles to the prevailing wind, when they are placed in valleys and urban areas they may not perform well.  Also the distribution over a country can vary considerably, for example in the UK, the average wind speed in exposed western coastal areas is higher than sheltered ones in the east. When comparing the experience of different countries, the variations in climate should be taken into account.

The base case for Doris is a location in the south of England (Koppen climate type: Cfb) with 1 kw each of wind and solar generating capacity.  The post "You have to do both" contains some discussion of a wind/solar based system might behave in this climate.

For this post, I've deliberately chosen some extreme and contrasting climates.  In the context of Doris, variations in climate come from the location which is used to provide estimates of clear sky solar irradiance, a crude model which provides some allowance for variations in cloud cover and weather reports from a local airfield.

The first graphic comes from a run where the base configuration was run with data from hot desert region (Koppen climate type: Bwh).  In this case, solar makes the greatest contribution to meeting the load, but at 35 deg. North of the equator, there is enough season variation to draw in supplies from the grid, the average wind speed in this place was low, so the yield from wind was low.

Based on the results from the base configuration, a possible adaption was to remove the wind generating component and double the solar capacity, the effect was to significantly reduce the draw down form conventionally generated sources.

At the other extreme is a northern continental area (Koppen climate type: Dfc) at a latitude of approximately 65 deg.  There is significant variation in the yield from solar over the year, this drops to zero in December and January, but provides a useful contribution in summer.  This arbitrarily selected location is not very windy and the contribution from wind even in winter is low resulting in a large draw down form the grid.

A logical adaption was to triple the wind generating capacity and halve the solar element, this still did not make a major reduction in grid draw down.

Since I have been studying sustainable energy sources, there is a recurring issue which might be termed the "winter problem".  In the northern hemisphere the demand for electricity peaks in the winter months whilst the supply can be low due to low solar radiance and periods of calm.  

Friday 4 September 2015

Doris - A thought experiment in progress (6) - The Mix

Doris is a thought experiment running on a Raspberry Pi and a laptop which is intended to explore sustainable energy, an evolving description and discussion can by found in a previous posts:


It is important to remember that Doris is a computer simulation with some arbitrarily set parameters and rules, it exists only in the imagination and has no physical reality.

Whilst scribbling lines of code, economics have deliberately been pushed into the background, but the loose assumption has been that both solar and wind generating capacity (excluding ancillaries like storage, inverters and bits of wire) each cost £1k/kw and the that the total cost is within sight of £5k and a lot of inconvenient issues have been ignored.   The rules which have been used suggest that a load of 2,500 kw/year can be matched with 2 kw of generating capacity.  The base configuration pretends it has 1 kw of wind and 1 kw of solar.  Changing the mix of wind and solar gives different results.

The objective is to minimize the dependency on conventionally generated sources.  These comments relate to a temperate maritime climate (Koppen: type Cfb), different climates would result in different results.  For example a hot desert (Koppen type: Bwh) would get a better yield from solar due to the relatively minor seasonal variations and the absence of cloud.  Subarctic climates (Koppen type: Dfb)
would get a low yield from solar due to the low sun and short days during winter months, in these places wind might offer a solution.

The graph shows the results of the simulations in which solar accounts for 25%, 50% and 75% of the generating capacity.  The graph shows maximum monthly draw down from conventional sources (which usually in December or January)  The dependency on conventional sources is lowest when the proportion of solar to wind is 25%/75%, this is primarily due to the low yield from solar during the winter.


In these simulations, the load is constant throughout the year, this is not realistic as the load would almost certainly be greater in winter than in summer  Future simulations may introduce seasonal variation in load, this will probably increase the amount of energy drawn from conventional sources with the low solar configuration.

A somewhat different results when the annual utilization of sustainable sources is plotted, in this case the high solar configuration uses approx. 82% of the energy it generates.  This is in part due to the high yield during the summer.  In its current form, the code gives priority to solar sources, but it is not thought that this has a major influence on the outcome (this may be tested in future simulations).