Archive for the ‘Environment’ Category

Estimating Rates of Air Change in Homes

June 6, 2021

Air flow in modern homes

Modern homes are built with low air leakage rates and then mechanically ventilated to keep the air ‘fresh’. To prevent heat losses associated with this air exchange, the outgoing ‘stale’ air is flowed through a heat exchanger to warm the incoming ‘fresh’ air.

However this Mechanical Ventilation with Heat Recovery (MVHR) is not suitable for many older homes – such as mine – which are too leaky.

My home has gaps between floorboards on the ground floor and the air can flow in and out easily through the underfloor void.

To seal my home to modern standards would require re-building the entire ground floor – adding insulation as one worked. One would then add MVHR to the newly-sealed house. This would be very disruptive, and so I have instead chosen to remain married.

So in my old house and many like it, heat losses from air flow are highly uncertain.

Wouldn’t it be great if there were some way to measure air flow in older homes which was cheap and convenient!

Measurement 

Air flow through a building is commonly characterised by the number of air changes per hour – ACPH. But how can this be measured if one doesn’t know where the air is coming in or going out?

This building wiki suggests:

Tracer gas measurement can be used to determine the average air change rate for naturally’-‘ventilated spaces’ and to measure infiltration (air tightness)’. To do this, a detectable, non-toxic gas is released into the space and the reduction in its concentration within the internal atmosphere is monitored over a given time period.’

By ‘tracer gas measurement’ the wiki means that a gas is released into the air at a known rate, and its concentration measured versus time. If the rate of production of tracer gas is known, then the final stable concentration allows one to work out the number of air changes per hour (ACPH).

  • If the number of ACPH is small, the final concentration will be high.
  • If the number of ACPH is high, the final concentration will be low.

What this wiki frustratingly fails to point out is that carbon dioxide is an ideal tracer gas and has been used for years for this purpose.

This essential fact is pointed out in the first paragraph of an outstandingly clear and authoritative paper from Andrew Persily and Lillian de Jonge.

Carbon dioxide generation rates for building occupants Persily A, de Jonge L. Indoor Air. 2017;27:868–879. https://doi.org/10.1111/ina.12383 . It’s also available with alternate formatting here.

The first line of the abstract is:

Indoor carbon dioxide (CO2) concentrations have been used for decades to characterize building ventilation and indoor air quality.

This surprised me because in all my reading about this subject in the UK I have never seen it mentioned. But then, in the first line of the paper itself, Persily and de Jonge point out just how old the idea is:

Indoor CO2 concentrations have been prominent in discussions of building ventilation and indoor air quality (IAQ) since the 18th century when Lavoisier suggested that CO2 build-up rather than oxygen depletion was responsible for “bad air” indoors.

The gist of their paper is a thorough review and examination of the factors which affect the rates at which human beings emit carbon dioxide. I won’t deprive you of the pleasure of reading the paper but factors discussed include:

  • The ratios of fat, protein and carbohydrate in people’s diet.
  • Age, gender and ethnicity.
  • Body size and mass.
  • Levels of activity.

The paper is very readable and I recommend it in the highest terms.

A worked example: my bedroom.

At night my wife and I sleep in a room which is about 7 m long, 3.5 m wide and 2.2 m high. So it has a volume of 7 x 3.5 x 2.2 = 54 cubic metres, or 54,000 litres.

There are no obvious draughts and I had no idea how many air changes per hour there were.

But overnight, the concentration of carbon dioxide rises from about 450 parts per million (ppm) characteristic of fresh air, and stabilises around 1930 ppm.

I can work out the number of air changes per hour ACPH using the formula below.

In this formula:

  • The room volume in litres
    • In my case 54,000 litres
  • c is the measured stable CO2 concentration in ppm
    • In my case 1930 ppm
  • c0 is the concentration of CO2 in ‘fresh’ air in ppm
    • In my case around 450 ppm
  • 10-6 is the scientific way of saying “divide by a million”
    • 1/1,000,000
  • CO2 production rate is what Persily and de Jonge’s paper tells us:
    • For sleeping males over the age of 11, the answer is within 10% of 12.7 litres per hour.
    • For sleeping females over the age of 11, the answer is within 10% of 10.2 litres per hour.
    • So our joint CO2 production rate is about 23 litres per hour

Putting all those numbers in the formula……we find the rate of change of air is around 0.29 ACPH – with the answer probably being within 10% of that value.

Some other factors.

Persily and de Jonge’s paper is extraordinarily thorough and tackles some of the tricky problems about using this technique for estimating air flow in buildings.

Firstly, there is the question of the level of activity of the people in a particular space. The metabolic rate is generally measured in units of mets with 1 met being roughly the metabolic activity during sleep. Very roughly it corresponds to around 58 watts.

The paper has extensive tables showing the CO2 production rate in litres per second for different levels of activity of different sexes at different ages. (Remember to multiply these numbers by 3600 to convert them into CO2 production rate in litres per hour before using them in the formula above.)

Secondly, there is the wider question of which volume of air is relevant. My bedroom represents a small volume with well understood rates of CO2 production.

But is a CO2 meter placed in a ground floor room measuring the characteristic concentration of the room it is in, the whole ground floor, or the entire house? Resolving questions like this may take a few experiments, such as moving the meter around.

Additionally, the amount of CO2 generated in a house over a day may not be clear. For example, the number of occupants and their level of activity may be hard to determine.

Mi casa no es tu casa

The situations encountered in your home will be different from those in my home.

Nonetheless, if you are trying to assess air flow within your home, I would recommend that you consider using carbon dioxide measurements as part of your arsenal of measurement techniques.

I use two CO2 meters and can recommend them both:

Air Conditioning versus Air Source Heat Pump

May 15, 2021

Click for a larger version. Similarities and differences in how an air source heat pump (ASHP) or an air conditioning (AC) system warms a home. All the components inside the dotted green line are contained in the external units shown. A key design difference is whether or not the working fluid is completely contained in the external unit. See text for more details.

Regular readers will probably be aware that – having reduced the heating demand in my house – my plan is to switch away from gas heating and install an electrically-powered air source heat pump to heat the house and provide domestic hot water.

But next week I am also installing air conditioning, something which is traditionally not thought of as very ‘green’. What’s going on?

Why Air Conditioning?

I have two reasons.

My first reason is that, as you may have heard, the whole world is warming up! Last year it reached 38 °C in Teddington and was unbearably hot for a week. I never want to experience that again.

During the summer the air conditioning will provide cooling. But assuming the heating comes with good weather, the air conditioning will be totally solar powered, and so it will not give rise to any CO2 emissions to make matters worse!

My second reason is that in the right circumstances, air conditioning is a very efficient way to heat a house. That’s what this article is about.

Heat Pumps

Air Conditioners (AC) and Air Source Heat Pumps (ASHP) are both types of heat pumps.

In scientific parlance, a heat pump is any machine that moves heat from colder temperatures to higher temperatures at the expense of mechanical work.

Note: to distinguish between the general scientific idea of a heat pump, and the practical implementation in an air source heat pump, I will use abbreviation ASHP when talking about the practical device.

The general idea of a heat pump is illustrated in the conceptual schematic below.

As shown, the pump uses 1 unit of mechanical energy to extract two units of heat energy from air at (say) 5 °C and expel all 3 units of energy (1 mechanical and 2 thermal) as heat into hot water at (say) 55 °C.

Click for a larger version. Traditional representation of the operation of heat pump.

Heat pumps can seem miraculous, but like all good miracles, they are really just applied science and engineering.

A heat pump is characterised using two parameters: COP and ΔT.

  • A heat pump which delivers 3 units of heat for 1 unit of work is said to have a coefficient of performance (COP) of 3.
  • The temperature difference between the hot and cold ends of the heat pump is usually called ‘Delta T’ or ΔT.

Obviously engineers would like to build heat pumps with high COPs, and big ΔTs and they have used all kinds of ingenious techniques to achieve this.

But it turns out that heat pumps only operate with high COPs when the ΔT is small and when the heating power is low. There are two reasons.

  • Firstly, the laws of thermodynamic set some absolute limits on the COP achievable for a given ΔT.
    • Most practical heat pumps don’t come close to this thermodynamic limit for a variety of mundane reasons.
    • The maximum COP for moving heat from 5 °C to 55 °C is 6.6.
    • The maximum COP for moving heat from 5 °C to 20 °C is 19.5.
  • Secondly, in order to heat a room to (say) 20 °C, the hot end of the heat pump needs to be hotter than 20 °C.
    • Typically the hot end of the heat pump must be 5 °C to 10 °C warmer than the room in order that heat will flow out of the heat pump.
    • Additionally the cold end of the heat pump must be 5 °C to 10 °C colder than the external air in order that heat will flow into the heat pump.
    • The interfaces between the ends of the pump and the environment are called heat exchangers and designing ‘good’ heat exchangers is tricky.
    • A ‘good’ heat exchanger is one that allows high heat flows for small temperature differences.

So now we have seen how heat pumps are characterised, let’s see how heat pumps are used domestically.

Air Source Heat Pump (ASHP) versus Air Conditioner (AC)

The schematic diagrams  below show how a house is heated by an ASHP and an AC system. Both systems operate using a working fluid such as butane, which is ingeniously compressed and expanded. The details of this process are not the topic of this article so here I am glossing over the fascinating details of the device’s operation. Sorry.

Click for a larger version. How an air source heat pump (ASHP) warms a home. All the components inside the dotted green line are contained in the external unit shown. A key design feature is that the working fluid is completely contained in the external unit and heat is transferred to the central heating water by a heat exchanger.

Click for a larger version. How an air conditioner (AC) warms a home. All the components inside the dotted green lines are contained in either the external unit or the fan coil unit shown. A key feature is that the working fluid itself flows into the fan coil unit and heats the air directly.

We can compare the operation of the two systems in the table below.

Air Conditioner Air Source Heat Pump
Air at (say) 5 °C is blown over a heat exchanger and evaporates the working fluid.

 

The same.
The working fluid is then compressed – that’s the bit where the work is done – and liquefies, releasing the captured heat.

 

The same.
The hot working fluid – now at ~30 °C then flows through a pipe to an indoor heat exchanger (fan coil unit) where air is blown over the pipe and heated to 20 °C. The hot working fluid – now at ~60 °C then flows through a heat exchanger and transfers the heat to water in my central heating system at ~55 °C
No corresponding step  

The 55 °C water then flows through a radiator in my room, heating the room by radiation and by convective heat transfer to air at ~20 °C.

Looking closely at the figures and table above, one can see that the operation of the ASHP and the AC system are broadly similar.

However the ASHP has to operate with a bigger ΔT (~55 °C versus ~25 °C) than the AC system, and also has to transfer heat through an extra heat exchanger.

Both these factors degrade the achievable COP and so for my application, the specified COP for an ASHP is just over 3, but for the AC system, it is just over 5.

In my well-insulated house, when the external temperature is 5 °C, I require typically 36 kWh per day of heating, equivalent to 1.7 kW continuous heating. I can achieve this in several ways:

  • Using gas I must burn ~40 kWh of gas at 90% efficiency costing 40 x 3p (£1.20) and emitting 40 x 200 g = 8 kgCO2
  • Using an ASHP with a COP of 3, I must use ~36 kWh/3 = 12 kWh of electricity costing 12 x 25p (£3.00) and emitting 12 x 200 g = 2.4 kgCO2
  • Using an AC system with a COP of 5, I must use ~36 kWh/5 = 7.2 kWh of electricity costing 7.2 x 25p (£1.80) and emitting 7.2 x 200 g = 1.4 kgCO2
  • Using a domestic battery and buying the electricity at night for 8p/kWh, I can reduce the cost of using an ASHP or AC system by a factor of 3 to £1.00/day or £0.60/day respectively.

[Note: In these calculations I have assumed that the carbon dioxide emissions per kWh are same for both gas and UK electricity (200 gCO2/kWh) which is roughly correct for 2021]

So using an AC system I should be able to achieve domestic heating with lower carbon dioxide emissions than an ASHP.

My plan

In my case I need to heat water for my home to 55 °C for use in showers and basins. So I need an ASHP for that. And since I already have radiators in every room, hooking up the ASHP to the radiator circuits is smart double use.

The AC system I am having installed will have 1 external unit and 2 internal ‘fan coil units’. One unit will be in my bedroom (a sheer indulgence) and the other will be high up on the stairs, allowing air to be either blown down to the ground floor where I hope it will circulate, or blown towards the bedrooms.

My hope is that, when used together, the AC system (COP~5) will reduce the heating output required from the radiators so that I can reduce the flow temperature of the water from 55 °C to perhaps 40 °C. This reduces their heat output, but increase the COP of the ASHP from 3 to perhaps 4.

The main difficulty that I foresee is the extent to which the AC heating will actually permeate through the house and so reduce the amount of heating required by the ASHP.

So I am not sure how much heating will be required by the ASHP acting through the radiators, and whether the radiators will work at low flow temperatures. It is possible I might need to replace a few radiators with ones which work better at low temperatures.

It is not at all obvious that this plan will actually work at all – but I think it is worth a try.

Kit

The air conditioning I am having installed is a Daikin 2MXM40 multi-split outdoor unit with two FTXM25 indoor air units. (Brochure)

The model of heat pump I will have installed is a Vaillant Arotherm plus 5 kW. It can supply up 5 kW of heating at 55 °C with a COP of 3  – i.e. it will use just 1.6 kW of electrical power to do that – and heat water to 55 °C. Water storage will be a 200 litre Unistor cylinder. A brochure with technical details can be found here, and a dramatic video showing the kit is linked at the end of this article.

When I have come to terms with how much money I am spending on this, I will share that information. But at the moment it hurts to think about it!

Anyway: the adventure begins next week!

 

Battery Day: One month on…

April 20, 2021

Click for larger version. The graph shows daily electricity drawn from the grid (kWh). Before the solar panels were installed average usage was 10.9 kWh/day. After the solar panels were installed this fell by a couple of kWh/day. After an increase over Christmas when our son returned, we were back to normal. In the last month the solar electricity generated on the lengthening days have reduced the electricity drawn from the grid. Since the battery was installed a month ago, we have not drawn any electricity from the grid, but daily usage has been unchanged.

Friends, I have been so busy I have been forgetting to blog! So I thought I’d just post a quick update on the Powerwall Installation.

One month on now and we are still “off grid”: we haven’t used a unit of grid electricity for a month. But as the graph above shows, our usage has continued unchanged.

My experience has convinced me that solar installations should all be accompanied by a battery of some sort.

As I have mentioned before, from a national perspective, local batteries are a bad idea: they consume energy.

But from from a user’s perspective, they allow me to benefit from my investment.

And the prospect of not using any grid electricity until maybe September leaves me giddy.

Longer term goals

Click for a larger version. Summary performance of the battery system on 17th April 2021. The battery supplies the house overnight, and is then re-charged by solar generation in the morning. When the battery is full (around 1 p.m.) solar generation is exported to the grid. In the evening the battery takes over again as the solar generation dwindles.

We haven’t experienced a summer of solar power yet, but the longer days are amazing – even in April!

On sunny days the panels generate over 20 kWh of electricity, and the battery is full in the middle of the day.

Once the battery is full, the system exports electricity to the grid in the afternoon and we don’t need to use the battery until perhaps 7 p.m.

These exports are an important part of our plan.

In the winter, we will need to buy electricity from the grid – especially as next winter we will be using a heat pump for heating and hot water.

But ideally we hope the exports of electricity in the summer should match imports in the winter.

Looking at the cumulative generation and export from the system (below), and remembering that exports should be larger in the summer, it looks like yearly exports might just reach 1000 kWh – much more than I had expected.

This would be enough to ‘balance’ 100 days of 10 kWh per day in the winter, assuming no solar power. But even in mid-winter there is typically 2 or 3 kWh per day and so we might just be able to achieve year-round balance.

Click for a larger version. Cumulative generation and export of solar electricity. The dotted green line was my initial guess for generation through the year.

Battery Day: First Results

March 20, 2021

Me and my new Tesla. This unit contains 13.5 kWh of battery storage along with a climate control system to optimise battery life. We have placed it in the porch so that (when visits are allowed again) everyone who visits will know about it!

Last September, Tesla held their ‘Battery Day‘ during which they unveiled their road map towards cheaper, better, batteries.

Not to be outdone, last Monday VW held their own ‘Battery Day‘ during which they unveiled their road map towards cheaper, better, batteries.

And last Thursday was my own battery day, when Stuart and Jozsef from The Little Green Energy Company came and installed a Tesla Powerwall 2 at Podesta Towers in Teddington. I was (and still am) ridiculously excited.

I am still evaluating it – obviously – but here are a couple of notes.

How it works

The system has two components. An intelligent ‘gateway’ that monitors loads and supplies, and a climate-controlled battery storage unit.

Click for a larger version. The left-hand graphic shows how AC power enters our house, and how DC power generated by solar panels is linked to the grid. When the Solar PV is sufficient ,power is exported to the grid. The right-hand graphic shows how the TESLA ‘gateway’ device monitors the solar PV, domestic loads and battery status and intelligently decides what to do.

The gateway (and the battery) are electrically situated between the electricity meter and all the loads and power sources in the house. So all energy enters or leaves the battery module as AC (alternating current) power.

But its internal batteries must be supplied with DC (direct current).

This makes it ideal for storing power from the AC grid, but less than ideal for storing the DC current generated by solar PV panels.

One might have expected that a device designed to store solar power might intrinsically operate using DC and indeed, some battery systems – positioned between the solar PV and the inverter – do this.

So the choice to place the Powerwall™ where it is, is a compromise between the extra functionality this location offers – it can back up the entire house – and the inefficiency of storing solar PV which is first converted to AC by the inverter, and then re-converted back to DC by the Powerwall. The support document states that the conversion from AC to DC and back to AC has 90% round-trip efficiency.

The photograph below shows the gateway installed under the stairs in our house.

Click for a larger version. The Tesla ‘Gateway’ installed in our house. The unit is positioned in between the electricity meter and all the domestic loads. The black conduit leads under the floor to the battery which is installed in the porch.

Control

The system is controlled by an app which is – frankly – mesmerising. It shows how electrical power flows between:

  • the grid,
  • the battery,
  • our home, and
  • our solar panels

Click for a larger version. Screenshots from the app at various times yesterday.

There is less room to adjust the parameters of the system than I had anticipated. This appears to be because, in exchange for a guarantee that the battery will retain at least 80% capacity (10.8 kWh) after 10 years, one is required to relinquish detailed control to the Tesla Brain.

Through a built in network connection, the device is in constant touch with Tesla who monitor its performance and can detect if it is abused in some way. I am not sure how I feel about that – but then guaranteed long-term performance is certainly worth something.

One feature of this relinquishing of detailed control concerns ‘time-of-use’ tariffs. I anticipate that – especially in winter – I will need to charge the battery overnight on cheap rate electricity.

The system supports this mode of operation but is not yet operational. Apparently it needs to study the patterns of household use for 48 hours before being enabled.

When operational, one gives the system general instructions and then allows it to choose when, and by how much, to charge. There is for example no way to force the battery to charge to 100% on command.

In practice I suspect it will be fine, but at the moment it still feels a little weird.

Performance on Day#1

The simplest way to show how the Powerwall™ works is by looking at the data which the ‘App’ makes available.

The first graph shows the household demand through the day. It’s fascinating to look at this data which has 5 minute and 0.1 kW resolution. The metrologist in me would like more – but in honesty, this is enough to understand what is happening.

Click for a larger graph. See text for details.

Now we can look to see how that demand was met. Overnight, we relied mainly on the grid.

Click for a larger graph. See text for details.

The battery could have supplied this overnight electricity, but it had been set to hold a reserve of 16% of its capacity (~2 kWh) in case we required backup after a power cut. We have lowered that setting now because, thankfully, power cuts are rare in Teddington. The battery drew power from the grid overnight in two short periods to maintain this reserve.

Additionally, at the end of a sunny day in which the solar PV filled the battery, there was brief period where we returned electricity to the grid.

During the day – which was very sunny 🙂 – the household electricity demand was met by the electricity from the solar panels.

Click for a larger graph. See text for details.

Without the battery, most of this 16.91 kWh of electricity would have been sent to the grid. But now only a tiny fraction was returned to the grid, most of it being captured by the battery – see below.

Click for a larger graph. See text for details.

The graph above shows the battery maintaining its reserve charge at night, and then charging from the solar PV during the day. At peaks of household demand, the charging is paused. At around 16:00, the battery was briefly full, and shortly thereafter it began discharging to meet household demand.

As I write this at 1:00 p.m. on the day after the day shown (a rather dull day 😦 ), the battery is 56% full and charging.

The graph below shows all the above curves together.

Click for a larger graph. See text for details.

Overall

The Powerwall system is an object of wonder. It is beautifully engineered and miraculous in its simplicity.

It transforms the utility of the solar PV allowing me (rather than electricity companies) to benefit from the investments I have made.

I will post more about the performance in terms of cost, electricity and carbon dioxide when I have more data.

But for the moment I will just thank Jozsef and Stuart from The Little Green Energy Company for their professionalism and attention to detail. And ‘No’. I am not being paid to say that – quite the opposite!

Stuart and Jozsef from The Little Green Energy Company. You can’t see it, but they assure me they were both smiling. Click for a larger version

 

Sometimes I find it hard to like EDF

March 16, 2021

Click for larger version. EDF wrote to me today to say they are increasing the price of ‘night rate’ electricity by 73.7%.

Energy is a wonderful thing. But sometimes it can be hard to like the companies which sell it to us…

…especially when they increase the price of their product by 73% overnight!

As regular readers will know, since 2018 I have been working hard to reduce my household energy consumption and the concomitant carbon dioxide emissions.

My 3-step plan has been:

  1. Reduce household heating requirement with insulation and triple-glazing.
  2. Switch from gas heating to electrical heating with a heat pump.
  3. Use solar panels and a battery to generate low-emission electricity and reduce the cost of switching to electrical heating.

Part#1 is complete: winter is not yet at an end, but heating demand appears to about 50% lower.

Part#2 is underway and I hope to have a heat pump installed this summer. But electrical heating is more expensive than gas heating.

Part#3 is underway: the solar panels are performing well and a battery should be installed this Thursday. The battery should allow me to use mainly my own solar electricity, or EDF off-peak electricity for most of the year.

I carried out extensive modelling of the effect of varying patterns of electricity consumption and compared different ‘tariffs’.

I had based my costings on the fact that the night rate for electricity would be about 5p/kWh and day rate would be about 25p/kWh. Of course I knew these costs could vary over time.

Nonetheless, it would be an underestimate to say that I was ‘disappointed’ when EDF wrote to me this morning to say that price of night time electricity was to rise from 4.99 p/kWh to 8.67 p/kWh…

…a 73% rise!

Like I said, sometimes it can be hard to like the companies which sell us energy.

Switch!

I am thinking about it.

But switching is, in my opinion, a distraction. It is a way of distracting us ‘fish’ from the fact that we are in a ‘barrel’ and at the mercy of the confusopolists.

 

Previous articles about the house.

2021

2020

2019

Domestic Batteries: Purchase decisions and realistic models

February 1, 2021

Friends, earlier this week I ordered a Tesla PowerWall 2 from the charming people at The Little Green Energy Company (TLGEC). They have given me a nominal installation date in late March 2021 and I will be sure to keep you updated.

So in my excitement I wrote another article about using batteries – and you can read it at length below. But AFTER I had spent hours calculating and graphing , I realised something very obvious but very profound.

  • The triple-glazing and external wall insulation have been ‘green’ investments. They avoid the need to burn fossil fuels.
  • The solar panels have been a ‘green’ investment. They produce low-carbon electricity.
  • The heat pump (when I install it) will be a ‘green’ investment. It will avoid the need to burn gas to heat the house.
  • But the battery is a financial investment. It will actually use extra electricity! However, it will lower the cost to me personally of making the ‘green’ investments.

My aim is to transition away from burning gas by using a heat pump. This switch requires me to use more electricity each year and without the financial savings that a battery yields this would be punitive.

More battery modelling: but using a climate re-analysis database!

I chose TLGEC over other installers because of their willingness – and ability – to answer tricky questions. And in one of their answers they gave me a jewel of link to this EU funded site with useful information about solar PV.

The site can be used like others to estimate the monthly generation from a solar PV installation. But unlike other sites the predictions are based on actual solar data over the period 2005-2016.

And uniquely – by using climate re-analysis –  it is possible to download this data for any location on Earth (!) to simulate hour-by-hour how a particular installation of panels would respond at any time during that period.

Click for a larger image. This web portal is available here.

This has enabled me to create models simulating the interaction of solar panels with a domestic battery similar to those I made previously. But instead of:

  • a minute-by minute model of a single day using simulated solar data,

I can now make…

  • an hour-by-hour model of an entire year using actual solar data.

Crucially this incorporates real-world (hour-to-hour and day-to day) variability which is one of the difficulties in trying to optimise the use of a battery.

The Model 

The Excel™ model (Solar Time Series Analysis 2005 – 2016 for Blog) is based (unsurprisingly) on a Tesla Powerwall 2 with 13.5 kWh of storage, but that can be changed in the file. Please note – this is not a simple model and is set up just for my panels in Teddington! If you want to use it for your site you will need to download data from the web portal above and place it in the spreadsheet.

The model has the following ‘features’ (default values shown in brackets)

  1. The electrical demand can have separate daily peak (1 kW) and off-peak (0.5 kW) values.
  2. The overnight charging rate can be changed (3 kW)
  3. The fractional filling of the battery in the morning can be changed seasonally between a summer value (100%) and a winter value (100%).
  4. The range of the ‘summer’ and ‘winter’ seasons can be defined (summer runs from day 60 to day 300)

The model evaluates:

  • The state of the charge of the battery hour-by-hour through the year,
  • The amount of peak and off-peak electricity which must be purchased to meet the required demand.
  • The amount of solar generation and the amount used on site, or exported.
  • The costs of different strategies.

One shortcoming of the model is that the 1-hour step is too long and so in some situations the model appears to overfill or underfill the battery. However I think the uncertainty this adds is relatively small.

The Parameters

I set the model to run with data both from individual years and from the average behaviour of all 12 years of data.

The demand I modelled was 0.5 kW overnight and 1 kW during the day. This is more than our house uses at present but is in line with the demand I expect when I install a heat pump to replace the gas boiler.

The model calculates the amount of electricity bought from the grid in both peak and off-peak periods and evaluates the fraction of demand met by solar electricity, and the cost.

I then investigated how different settings for the morning filling of the battery affected:

  • the amount of electricity bought from the grid (peak and off-peak) over the year,
  • the fraction of demand met by solar electricity,
  • the cost.

Typical Runs

The graph below shows the simulated State of Charge (SoC) of the battery during days 1 to 30 of the year 2016 i.e. January 2016.

Click for a larger view.

The graph shows daily overnight charging of the battery to 100% in the morning. The 1-hour time resolution of the simulation makes it appear the battery does not quite completely fill up, but it gets close.

The 1 kW daytime load then drains the battery completely on most days – the SoC reaches zero – and so some full price electricity must be bought.

However, there are a few days (e.g. days 7 & 8 and days 13 to 16) even in January in which strong sunlight fills the battery sufficiently that it lasts to the end of the day. These would typically be cold, crisp, clear winter days.

To indicate the variability, the equivalent graph for the year 2011 is shown below.

Click for a larger view.

But if we plot the average data from 2005 to 2016 we see it has a different character from that for individual years. Instead of the 3 or 4 bright sunny days, we have – on average – a little bit of sunshine on many more days.

Click for a larger view.

This difference between individual years and their average is important in this case, because it the intermittency of solar generation that makes a battery useful, and it is the irregularity of solar generation in any one year that makes it hard to optimise the use of a battery.

A whole year of averaged data is shown in the graph below. I have used average data to illustrate the general characteristics of the behaviour of the battery.

Click for a larger view.

In this graph the battery is charged each night to 100% SoC. In the winter it discharges through the day and the SoC reaches zero before the end of the day, requiring full price grid electricity to tide the household over to the end of the day and the start of cheap electricity.

But between days 60 and 300 there is enough solar generation – on average – such that the battery does not ever fully discharge at the end of each day. Thus in this period is not really necessary to fully charge the battery overnight.

The graph below shows the effect of only charging the battery to 70% in the mornings over this ‘summer’ period.

Click for a larger view.

The result of this is that less night-time electricity is used, and less electricity is exported. Consequently, the ‘self-use’ of solar electricity increases. However, there are now a few more occasions during the ‘summer’ when the  SoC reaches zero before the end of the day i.e. where full price electricity must be bought.

The graph below shows the same partial-charging strategy (only 70% between days 60 and 300) but using data for the year 2011: notice that the irregularity is much greater than when looking at the averaged data.

Click for a larger view.

So how does one make sense of all this? I do not want to spend my entire life optimising battery charging!

Basic Results

There are too many variables to succinctly summarise the modelling results, so here I will just summarise one investigation relevant to my own situation.

Imagining that I am running a heat pump to replace the gas boiler, I have assumed overnight use at 0.5 kW and daytime use at 1.0 kW. This amounts to 21 kWh/day or 7665 kWh/year. Due to the limited time step, the model calculates annual use as 7661 kWh – which is an error of 0.05%.

Using the solar data for each individual year – and for the average of all the years – I calculated how self-use of solar power varied as I changed the state of charge (SoC) of the battery in the morning from 0% to 100%.

By ‘self-use’ I mean that the solar electricity was either used immediately at the house or stored in the battery for later use. Nominally either of these uses is ‘free’, but in reality the storage and retrieval is only around 90% efficient.

Result#1

First of all looking at solar data from each year 2005 to 2016 I calculated that on average the panels would generate 3847 kWh/year with a standard deviation of about 5%. The average value is same as is calculated from just using the average 2006-2016 datset

Click for a larger view.

The solar generation is only around half of the anticipated demand (see below). And without a battery, most of that is exported at a relatively low price (1.8 p/kWh from EDF). This benefits the planet and EDF, but means I still have to pay EDF 23.7 p/kWh for peak time electricity to operate the heat pump.

Click for a larger view.

Next – using the solar data for each individual year – and for the average of all the years – I calculated how self-use of solar electricity varied as I changed the state of charge (SoC) of the battery in the morning from 0% to 100%.

Click for a larger view. The graph shows the number of units of solar electricity (kWh) that would have been used on site.

If we pick one year (say 2014) as an example, we that in this sunnier-than-average year, charging the battery to about 30% SoC in the morning leaves plenty of capacity to store solar electricity during the day.

In a more typical year (say 2016) the optimum morning SoC is between 40% and 50%.

  • Higher morning SoC results in solar generation being ‘lost’ to export.
  • Lower morning SoC will give rise to earlier discharge of the battery and the use of more mains electricity.

Curiously, the optimum morning SoC for any individual year (30% to 60%) is quite different from that calculated from the average of all 12 years. This is because of reduced irregularity in the averaged data.

The difference between self-use calculated from data for individual years and the self-use calculated from the average data is even more striking if we show each year’s result as a fraction of that year’s total generation.

Click for a larger view. The graph shows the fraction of total solar generation (%) that would have been used on site for each year.

We see that we might hope to get around 90% of self-use in any individual year with a morning SoC of around 40%. This is much lower than the 98% which appears possible using averaged data.

Results: Economics 101

As I whiled away happy hours with Excel I became fascinated by different possible strategies. And I filled my head with clever calculations that I might attempt.

But then I realised that none of these strategies affects the carbon reduction I achieve by installing solar panels. This happens with or without a battery and is independent of the charging strategy I adopt!

  • What these charging strategies affect is who gets the benefit!

If I export electricity at low cost (1.8 p/kWh in the case of EDF) and am then forced to buy electricity later in the day for 23.7 p/kWh (EDF) then it is EDF who gets the benefit of my investment.

Financially, the optimum strategy arises from the differences between night-time and day-time electricity, and the price paid for exports. I have illustrated this for two ‘tariffs’ below – those from EDF and those from Tesla – who have a deal with Octopus.

Click for a larger view.

If I simply bought the electricity from EDF without solar panels, then the annual cost would be just over £1600.

The solar panels should reduce this cost substantially. The investment of £4200 in the solar panels should generate a saving of around £500/year, a 12% return on investment.

The battery should lower the annual cost much further. The savings generated by this £10,000 investment should be more than £800/year.

  • Using the EDF tariff, the big difference between the price of day-time and night-time electricity makes it always preferable to have a morning SoC as high as possible, thus minimising the possibility of ever having to use full-price electricity.
  • Using the Tesla tariff – the morning SoC doesn’t matter because there is no time-of-day price difference, and no difference in price between imports and exports.

But using either tariff, I calculate the savings to be massive. So large in fact that I just can’t believe them! The battery should be installed in March and I will let you know how it goes!

Of course I could also lower the cost by switching from EDF. I checked with Octopus energy (link) and it listed 80 different tariffs. Eighty! Enough for 10 octopuses to each have a tariff for each leg.  I absolutely detest this confusopoly. In any case the cheapest night time price was around 11p. Hopefully with the battery I will be able to subsist mainly on EDF’s night-time tariff.

Summary

So after all that work, I realised something very obvious but very profound. As I said at the top the article:

  • The triple-glazing and external wall insulation have been ‘green’ investments. They avoid the need to burn fossil fuels.
  • The solar panels have been a ‘green’ investment. They produce low-carbon electricity.
  • The heat pump (when I install it) will be a ‘green’ investment. It will avoid the need to burn gas to heat the house.
  • But the battery is a financial investment. It will actually use extra electricity! However, it will lower the cost to me personally of making the ‘green’ investments.

External Wall Insulation: How it’s done.

November 11, 2020

As many of you will know, I am having External Wall Insulation (EWI) applied to my house.

As closer confidantes will confirm: I am obsessed with the project. Why? Because based on my calculations, it is the single-most effective thing one can do to an old house to improve its thermal performance and reduce carbon dioxide emissions.

And yet very few people seem to be doing it. My hope is that by simply talking about it – and by measuring how effective it really is – more people will consider it as an option.

The idea of EWI is simple – “just stick insulating materials to the outside of a house“. But the reality of doing this reliably and leaving the house weatherproof and looking good is complex.

There are some nice videos out there, such as this one below showing the Be Constructive team working on a previous house. There are more videos here. And if you want details, then check out the extensive EWIPro Complete Guide (pdf) and all the materials are available at the EWI Store.

But partly for my own satisfaction I thought I would outline each step with pictures rather than video. Also, the video shows the application of expanded polystyrene boards and the procedure for the polyurethane foam boards that I have used is a little different.

So here is my description the process. There is a gallery of photographs at the end of the article.

Step 1: Preparation

The job began by protecting all the working surfaces – the patio and the front and rear gardens – with protective plastic, and then all the windows were covered with a transparent adhesive film.

For my house, the Be Constructive team demolished an old chimney which no longer had a reason for existing, and removed almost 2 tonnes of loose render from the side wall. So much render was removed that the wall had to be roughly re-rendered before they could begin applying the EWI.

They then moved the boiler exhaust, external electrical fittings and drain pipes to take account of the fact that the house was about to grow by about 120 mm in all directions. This stuff is rather tedious – but essential.

Next came the preparation of the outside walls and the painting of a ‘stabilizing primer’. This penetrates porous surfaces and binds them, creating a surface to which adhesive can stick. This is particularly important for some building blocks which can be quite powdery.

Step 2: Boarding. Kingspan K5

Next the team installed so-called ‘starter track’. This plastic support is screwed into the wall at the level of the first layer of insulating boards – usually just above the damp proof course – and makes sure the boards are horizontal, and supports them while the adhesive mortar dries.

Different stages in the application External Wall Insulation. Click for a larger version.

Normally EWI utilises either expanded polystyrene (sometimes abbreviated as XPS or EPS) or Rockwool™, and boards made from these materials are available in a wide range of thicknesses.

However I had asked to use a board made by Kingspan called K5. I chose this because I could only put about 100 mm thickness around the house – and for a given thickness, K5 will give the best insulation.

I limited the insulation to 100 mm because that amount would still keep the walls underneath the existing ‘soffit’ under the eaves. Also – if the insulation were much deeper – I felt the windows might seem to be too recessed.

Only 100 mm thickness of Insulating Boards would fit under the eaves of my house. Click for a larger image.

For some reason, 100 mm thick boards of Kingspan K5 were not available and so the Be Constructive team glued pairs of 50 mm thick boards together to achieve the required thickness.

The ‘double’ boards were stuck to the wall using several thick blobs of adhesive mortar. Using a big blob of mortar perhaps 10 mm deep allows the outer surfaces of the boards to be made parallel even when the underlying wall is not.

In my illustrations I have deliberately drawn the boards as being not parallel. In fact the Be Constructive team actually took a lot of care into making the final surfaces vertical and smooth. This is important because it is very difficult to compensate for this after the fact.

The boards are ‘overlapped’ at corners and cut to shape around windows and other architectural features. Any gaps are filled in with expanding foam.

Insulating Boards are overlapped at corners. Click for a larger image.

Step 3: Mechanical Fixing.

Once the boards are stuck to the wall and the mortar has set, the boards are mechanically fixed in place. To achieve this a hole is drilled through the boards and into the wall. Then a plastic fixing is pushed into the hole. Finally a metal nail is hammered into the plastic fixing which locks the plastic fixing in place – like a rawlplug – and holds the boards against the wall.

Using metal nails adds a heat leak directly through the boards: each fixture increases the thermal transmittance of the board by about 3%. However there is not much that can be done about that. It would be unwise to rely solely on the mortar or just plastic fixings.

Step 4: Base Coat Layers

Now the boards are attached to the wall and functionally insulating the house. But they are neither weatherproof nor attractive.

Preparatory stages in the application of weatherproof render. Click for a larger version.

So the next step is to coat the boards with an adhesive mortar (called a ‘base coat’) in which a glass-fibre mesh is embedded. This mesh is essential to prevent cracking due to building movement.

For polystyrene insulation this is a simple process: the boards are rasped to create a smooth surface; a layer of base coat is applied; the mesh is pressed into place; and then the mortar is smoothed. This forms a surface on which the the final render can be applied.

For K5 insulation, the process is more complicated because the surface of the boards should not be abraded. So:

  • First a thin layer of the base coat is applied to boards to create a smooth surface.
  • Then a second layer of base coat is applied into which the fibre-glass mesh is pressed.
  • Finally a third layer of base coat is applied to form a surface on which the final render can be applied.

The base coat also meshes with the corner and reveal ‘beads’, and with extra fibre-glass mesh placed around the corners of windows.

Step 5: And finally

And finally we come to the point where render is applied.

The render is a mixture of stone with a specifiable particle size: 1 mm, 1.5 mm or 2 mm , together with a mortar and a silicone polymer. It can be coloured in a very wide range of colours.

Additionally, my house will have ‘faux’ bricks called ‘brick slips’ applied to match architectural details on neighbouring buildings.

I’ll be sure to post pictures when we have finished.

 

Anticipated general look of the front of our house after rendering. Click for a larger view.

Photo Gallery – click for a larger version

Does it work?

But does it work? Well, of course it works! It would be physically impossible for it not to work!

The question isHow well does it work?“. And specifically, “Does it work as well I anticipated in my modelling?

These are complicated questions to answer definitively – and they are especially difficult to answer quickly.

I will not have a definitive answer until later in the winter, but I will explain how I will answer the question in a follow-up article. For now I will just tease you with the answer that the data look ‘promising’.

Keep warm 🙂

External Wall Insulation: the project begins…

October 15, 2020

I just wanted to take a moment to let people know that amidst the world’s problems, good stuff is happening.

And for me that good stuff is the start of installation of my long-awaited External Wall Insulation.

The idea of insulating a house from the outside is very simple in principle. In fact it is obvious. But in practice there are many steps required to ensure a weather-proof installation that will look smart when finished and last for a long time. That’s why I have hired the professionals at Be Constructive to do it!

Here are some pictures of the project in progress.

The back of my house. Click for a larger view

The picture above shows the general view of the back of the house with the insulation partially applied. Notice the dark red paint on the existing wall which enhances the adhesion of the insulation and the way the insulation is carefully cut out around the windows and the external tap and water drain.

The picture below shows the external tap and drain which have been extended beyond the thickness of the insulation. Expanding foam has been used to keep the system air-tight.

Detail showing the way the external pipes are extended to allow for the insulation. Click for larger version.

The picture below shows more detail of the window including the protective films applied during construction. You can see white ‘webbing’ around the windows which is called an ‘APU bead’ and its clever function (described in this video) is to give a neat seal between the window and the render.

Details of the insulation around a window. Click for a larger version.

The picture below details of the way the insulation is initially applied to the wall. The insulation consists of two boards of 50 mm thick Kingspan K5 which have been glued together to make a ‘dual-board’ which is 100 mm thick. This ‘dual-board’ is then stuck to the wall using an adhesive mortar. When this is dry – perhaps tomorrow? – the boards will be mechanically fixed to the wall using low thermal conductivity supports.

Details of the way the insulation is initially stuck to the wall. Click for a larger version.

Having considered this for this for such a long time, I am very excited to finally be making progress.

Within a few months I will find out whether or not my calculations were correct! I will keep you updated.

Previous articles on this topic

2020

2019

I hate it when it’s too hot

August 7, 2020

 

I find days when the temperature exceeds 30 °C very unpleasant.

And if the night-time temperature doesn’t fall then I feel doubly troubled.

I have had the feeling that such days have become more common over my lifetime. But have they?

The short  summary is “Yes”. In West London, the frequency of days on which the temperature exceeds 30 °C has increased from typically 2 days per year in the 1950’s and 1960’s to typically 4 days per year in the 2000’s and 2010’s. This was not as big an increase as I expected.

On reflection, I think my sense that these days have become more common probably arises from the fact that up until the 1980’s, there were many years when such hot days did not occur at all. As the graph at the head of the article shows, in the 2010’s they occurred every year.

Super-hot days have now become normal.

You can stop reading at this point – but if you want to know how I worked this out – read on. It was much harder than I expected it would be!

Finding the data

First, please notice that this is not the same question as “has the average summer temperature increased?”

A single very hot day can be memorable but it may only affect the monthly or seasonal average temperatures by a small amount.

So one cannot merely find data from a nearby meteorological station….

…and plot it versus time. These datasets contain just the so-called ‘monthly mean’ data. i.e.. the maximum or minimum daily temperature is measured for a month and then its average value is recorded. So individual hot days are not flagged in the data. You can see my analysis of such data here.

Instead one needs to find the daily data – the daily records of individual maximum and minimum temperatures.

Happily this data is available from the Centre for Environmental Data Analysis (CEDA). They host the Met Office Integrated Data Archive System (MIDAS) for land surface station data (1853 – present). It is available under an Open Government Licence i.e. it’s free for amateurs like me to play with.

I registered and found the data for the nearby Met Office station at Heathrow. There was data for 69 years from 1948 to 2017, with a single (comma separated variable) spreadsheet for maximum and minimum temperatures (and other quantities) for each year.

Analysing the data

Looking at the spreadsheets I noticed that the 1948 data contained daily maxima and minima. But all the other 68 spreadsheets contained two entries for each day – recording the maximum and minimum temperatures from two 12-hour recording periods

  • the first ended at 9:00 a.m. in the morning: I decided to call that ‘night-time’ data.
  • and the second ended at 9:00 p.m. in the evening: I decided to call that ‘day-time’ data.

Because the ‘day-time’ and ‘night-time’ data were on alternate rows, I found it difficult to write a spreadsheet formula that would check only the appropriate cells.

After a day of trying to ignore this problem, I resolved to write a program in Visual Basic that could open each yearly file, read just a relevant single temperature reading from each alternate line, and save the counted the data in a separate file.

It took a solid day – more than 8 hours – to get it working. As I worked, I recalled performing similar tasks during my PhD studies in the 1980’s. I reflected that this was an arcane and tedious skill, but I was glad I could still pay enough attention to the details to get it to work.

For each yearly file I counted two quantities:

  • The number of days when the day-time maximum exceeded a given threshold.
    • I used thresholds in 1 degree intervals from 0 °C to 35 °C
  • The number of days when the night-time minimum fell below a given threshold
    • I used thresholds in 1 degree intervals from -10 °C to +25 °C

So for example, for 1949 the analysis tells me that there were::

  • 365 days when the day-time maximum exceeded 0 °C
  • 365 days when the day-time maximum exceeded 1 °C
  • 363 days when the day-time maximum exceeded 2 °C
  • 362 days when the day-time maximum exceeded 3 °C
  • 358 days when the day-time maximum exceeded 4 °C
  • 354 days when the day-time maximum exceeded 5 °C

etc…

  • 6 days when the day-time maximum exceeded 30 °C
  • 3 days when the day-time maximum exceeded 31 °C
  • 0 days when the day-time maximum exceeded 32 °C
  • 0 days when the day-time maximum exceeded 33 °C
  • 0 days when the day-time maximum exceeded 34 °C

From this data I could then work out out that in 1949 there were…

  • 0 days when the day-time maximum was between 0 °C and 1 °C
  • 2 days when the day-time maximum was between 1 °C and 2 °C
  • 4 days when the day-time maximum was between 2 °C and 3 °C
  • 4 days when the day-time maximum was between 3 °C and 4 °C

etc..

  • 3 days when the day-time maximum was between 30 °C and 31 °C
  • 3 days when the day-time maximum was between 31 °C and 32 °C
  • 0 days when the day-time maximum was between 32 °C and 33 °C
  • 0 days when the day-time maximum was between 33 °C and 34 °C

Variable Variability

As I analysed the data I found it was very variable (Doh!) and it was difficult to spot trends amongst this variability. This is a central problem in meteorology and climate studies.

I decided to reduce the variability in two ways.

  • First I grouped the years into decades and found the average numbers of days in which the maximum temperatures lay in a particular range.
  • Then I increased the temperature ranges from 1 °C to 5 °C.

These two changes meant that most groups analysed had a reasonable number of counts. Looking at the data I felt able to draw four conclusions, none of which were particularly surprising.

Results: Part#1: Frequency of very hot days

The graph below shows that at Heathrow, the frequency of very hot days – days in which the maximum temperature was 31 °C or above has indeed increased over the decades, from typically 1 to 2 days per year in the 1950’s and 1960’s to typically 3 to 4 days per year in the 2000’s and 2010’s.

I was surprised by this result. I had thought the effect would be more dramatic.

But I may have an explanation for the discrepancy between my perception and the statistics. And the answer lies in the error bars shown on the graph.

The error bars shown are ± the square root of the number of days – a typical first guess for the likely variability of any counted quantity.

So in the 1950’s and 1960’s it was quite common to have years in which the maximum temperature (at Heathrow) never exceeded 30 °C. Between 2010 and 2017 (the last year in the archive) there was not a single year in which temperatures have not reached 30 °C.

I think this is closer to my perception – it has become the new normal that temperatures in excess of 30 °C occur every year.

Results: Part#2: Frequency of days with maximum temperatures in other ranges

The graph above shows that at Heathrow, the frequency of days with maxima above 30 °C has increased.

The graphs below shows that at Heathrow, the frequency of days with maxima in the range shown.

  • The frequency of ‘hot’ days with maxima in the range 26 °C to 30 °C has increased from typically 10 to 20 days per year in the 1950s to typically 20 to 25 days per year in the 2000’s.

  • The frequency of ‘warm’ days with maxima in the range 21 °C to 25 °C has increased from typically 65 days per year in the 1950s to typically 75 days per year in the 2000’s.

  • The frequency of days with maxima in the range 16 °C to 20 °C has stayed roughly unchanged at around 90 days per year.

  • The frequency of days with maxima in the range 11 °C to 15 °C appears to have increased slightly.

  • The frequency of ‘chilly’ days with maxima in the range 6 °C to 10 °C has decreased from typically 70 days per year in the 1950’s to typically 60 days per year in the 2000’s.

  • The frequency of ‘cold’ days with maxima in the range 0 °C to 5 °C has decreased from typically 30 days per year in the 1950’s to typically 15 days per year in the 2000’s.

Taken together this analysis shows that:

  • The frequency of very hot days has increased since the 1950’s and 1960’s, and in this part of London we are unlikely to ever again have a year in which there will not be at least one day where the temperature exceeds 30 °C.
  • Similarly, cold days in which the temperature never rises above 5 °C have become significantly less common.

Results: Part#3: Frequency of days with very low minimum temperatures

While I was doing this analysis I realised that with a little extra work I could also analyse the frequency of nights with extremely low minima.

The graph below shows the frequency of night-time minima below -5 °C across the decades. Typically there were 5 such cold nights per year in the 1950’s and 1960’s but now there are more typically just one or two such nights each year.

Analogous to the absence of years without day-time maxima above 30 °C, years with at least a single occurrence of night-time minima below -5 °C are becoming less common.

For example, in the 1950’s and 1960’s, every year had at least one night with a minimum below -5 °C at the Heathrow station. In the 2000’s only 5 years out 10 had such low minima.

Results: Part#4: Frequency of days with other minimum temperatures

For the Heathrow Station, the graphs below show the frequency of days with minima in the range shown:

  • The frequency of ‘cold’ nights with minima in the range -5 °C to -1 °C has decreased from typically 45 days per year in the 1950’s to typically 25 days per year in the 2000’s.

  • The frequency of ‘cold’ nights with minima in the range 0 °C to 4 °C has decreased from typically 95 days per year in the 1950’s to typically 80 days per year in the 2000’s.

  • The frequency of nights with minima in the range 5 °C to 9 °C has remained roughly unchanged.

  • The frequency of nights with minima in the range 10 °C to 14 °C has increased from typically 90 days per year in the 1950’s to typically 115 days per year in the 2000’s.

  • The frequency of ‘warm’ nights with minima in the range 15 °C to 19 °C has increased very markedly from typically 12 days per year in the 1950’s to typically 30 days per year in the 2000’s.

  • ‘Hot’ nights with minima in the above 20 °C are still thankfully very rare.

 

Acknowledgements

Thanks to Met Office stars

  • John Kennedy for pointing to the MIDAS resource
  • Mark McCarthy for helpful tweets
  • Unknown data scientists for quality control of the Met Office Data

Apologies

Some eagle-eyed readers may notice that I have confused the boundaries of some of my temperature range categories. I am a bit tired of this now but I will sort it out when the manuscript comes back from the referees.

Research into Nuclear Fusion is a waste of money

November 24, 2019

I used to be a Technological Utopian, and there has been no greater vision for a Technical Utopia than the prospect of limitless energy at low cost promised by Nuclear Fusion researchers.

But glowing descriptions of the Utopia which awaits us all, and statements by fusion Utopians such as:

Once harnessed, fusion has the potential to be nearly unlimited, safe and CO2-free energy source.

are deceptive. And I no longer believe this is just the self-interested optimism characteristic of all institutions.

It is a damaging deception, because money spent on nuclear fusion research could be spent on actual solutions to the problem of climate change. Solutions which exist right now and which could be implemented inside in a decade in the UK.

Reader: Michael? Are you OK? You seem to have come over a little over-rhetorical?

Me: Thanks. Just let me catch my breath and I’ll be fine. Ahhhhhh. Breathe…..

What’s the problem?

Well let’s just suppose that the current generation of experiments at JET and ITER are ‘successful’. If so, then having started building in 2013:

  • By 2025 the plant should be ready for initial plasma experiments.
  • Unbelievably, full deuteriumtritium fusion experiments will not start until 2035!
    • I could not believe this so I checked. Here’s the link.
    • I can’t find a source for it, but I have been told that the running lifetime of ITER with deuterium and tritium is just 4000 hours.
  • The cost of this experiment is hard to find written down – ITER has its own system of accounting! – but will probably be around 20 billion dollars.

And at this point, without having ever generated a single kilowatt of electricity, ITER will be decommissioned and its intensely radioactive core will be allowed to cool down until it can be buried.

The ‘fusion community’ would then ask for another 20 billion dollars or so to fund a DEMO power station which might be operational around 2050. At which point after a few years of DEMO operation, commercial designs would become available.

So the overall proposal is to spend about 40 billion dollars over the next 30 years to find out if a ‘commercial’ fusion power station is viable.

This plan is the embodiment of madness that could only be advocated by Technological Utopians who have lost track of the reason that fusion might once have been a good idea.

Let’s look at the problems in the most general terms.

1. Cost

Fusion will not be cheap. If we look at the current generation of nuclear fission stations, such as Hinkley C, then these will cost around £20 billion each.

Despite the fact the technology for building nuclear fission reactors is now half a century old, previous versions of the Hinkley C reactor being built at Olkiluoto and Flamanville are many years late, massively over-budget and in fact may never be allowed to operate.

Assuming Hinkley C does eventually become operational, the cost of the electricity it produces will be barely affected by the fuel it uses. More than 90% of the cost of the electricity is paying back the debt used to finance the reactor. It will produce the most expensive electricity ever supplied in the UK.

Nuclear fusion reactors designed to produce a gigawatt of electricity would definitely be engineering behemoths in the same category of engineering challenge as Hinkley C, but with much greater complexity and many more unknown failure modes. 

ITER Project. Picture produced by Oak Ridge National Laboratory [CC BY 2.0 (https://creativecommons.org/licenses/by/2.0)]

The ITER Torus. The scale and complexity is hard to comprehend. Picture produced by Oak Ridge National Laboratory [CC BY 2.0 (https://creativecommons.org/licenses/by/2.0)%5D

Even in the most optimistic case – an optimism which we will see is not easy to justify – it is inconceivable that fusion technology could ever produce low cost electricity.

I don’t want to live in a world with
nuclear fusion reactors, because
I don’t want to live in a world
where electricity is that expensive.
Unknown author

2. Sustainable

One of the components of the fuel for a nuclear fusion reactor – deuterium – is readily available on Earth. It can be separated from sea water at modest cost.

The other componenttritium – is extraordinarily rare and expensive. It is radioactive with a half-life of about 10 years.

To  become <irony>sustainable<\irony>, a major task of a fusion reactor is to manufacture tritium.

The ‘plan’ is to do this by bombarding lithium-6 with neutrons causing a reaction yielding tritium and helium.

Ideally, every single neutron produced in the fusion reaction would be captured, but in fact most of them will not be lost. Instead, a ‘neutron multiplication’ process is conceived of, despite the intense radioactive waste this will produce.

3. Technical Practicality

I have written enough here and so I will just refer you to this article published on the web site of the Bulletin of Atomic Scientists.

This article considers:

  • The embedded carbon and costs
  • Optimistic statements of energy balance that fail to recognise the difference between:
    • The thermal energy of particles in the plasma
    • The thermal energy extracted – or extractable.
    • The electrical energy supplied for operation
  • Other aspects of the tritium problem I mentioned above.
  • Radiation and radioactive waste
  • The materials problems caused by – putatively – decades of neutron irradiation.
  • The cooling water required.

I could add my own concerns about neutron damage to the immense superconducting magnets that are just a metre or so away from the hottest place in the solar system.

In short, there are really serious problems that have no obvious solution.

4. Alternatives

If there were no alternative, then I would think it worthwhile to face down all these challenges and struggle on.

But there are really good alternatives based on that fusion reactor in the sky – the Sun.

We can extract energy directly from sunlight, and from the winds that the Sun drives around the Earth.

We need to capture only 0.02% of the energy in the sunlight reaching Earth to power our entire civilisation!

The complexity and cost of fusion reactors even makes fission reactors look good!

And all the technology that we require to address what is acknowledged as a climate emergency exists here and now.

By 2050, when (optimistically?) the first generation of fusion reactors might be ready to be built – carbon-free electricity production could be a solved problem.

Nuclear fusion research is, at its best, a distraction from the problem at hand. At worst, it sucks money and energy away from genuinely renewable energy technologies which need it.

We should just stop it all right now.


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