Assessing Powerwall battery degradation: End of Winter of 22/23

Friends, three months ago in December 2022, I wrote about my technique for estimating the degradation of the capacity of my Tesla Powerwall 2 battery (Link).

The idea was to consider data from days in which the battery is charged to 100% capacity at night, and then discharges fully to 0% during the day. This only happens in winter when household demand is high and solar PV generation is low. After correcting for any solar generation one can make a reasonable estimate for the practical working capacity of the battery.

Click on diagram for a larger version. Illustration of day in which the Powerwall fully discharged.

We have now had another 60 days during which the battery discharged fully and using the same technique I described previously, I have re-evaluated the degradation of battery capacity. The results are shown below in two graphs: it’s the same data in both graphs they are just plotted on different times scales.

Taking all the data into account, the trend line suggests that battery capacity is degrading at roughly 2.6% per year. Since the battery has a nominal capacity of 13.5 kWh, this corresponds to a loss of capacity of 0.35 kWh/year.

Click on image for a larger version. Full discharge capacity plotted versus date. The blue dots show all the data and the dots surrounded with a pink circle show data with solar contribution during the day. The trend line suggests capacity is being lost at 2.6%/year.

The Powerwall was installed in March 2021 and if the degradation were to continue at 2.6%/year in future years then the battery would lose 10% of its capacity by the winter of 2024, and 20% of its capacity by the winter of 2028.

Click on image for a larger version. Full discharge capacity plotted versus date. The blue dots show all the data and the dots surrounded with a pink circle show data with solar contribution during the day. The trend line suggests capacity is being lost at 2.6%/year.

What to do?

Nobody wants their £10,000 battery to be losing capacity. But there is very little I can do about it! It’s inherent in the nature of the batteries and of the charge/discharge cycles they experience.

One option would be to prevent the battery from fully discharging by forcing it to retain, say, 1 kWh in reserve. However, while this might reduce the rate of degradation, it would deprive me of the battery capacity that I was hoping to preserve!

So my plan is to do nothing. If the degradation continues at this rate I will still have a 10 kWh battery in 2031 – and that’s still a very useful size of battery.

If I feel the need for more storage, my thought is that it would probably eventually make sense to upgrade one of my solar PV inverters to be a hybrid inverter, and then add extra battery capacity in the loft. These batteries will have the Lithium Iron Phosphate chemistry that is supposed have very low degradation. This option would likely cost much less than buying a replacement – or additional – Powerwall.

Of course, by the time this becomes important Powerwalls may have fallen in price and be readily available (!). Or the use of batteries in vehicles for domestic storage may have become commonplace.

In short, this is tomorrow’s problem.

 

Tariff Calculation Spreadsheet

Friends, as you may or may not know, Saturday night is the night of week I like spend documenting large spreadsheets. Lucky me!

In this article I will be describing how to use the spreadsheet I have developed over the last couple of weeks and used in the last couple of articles. The spreadsheet allows one to estimate the likely costs of using particular electricity tariffs – the Octopus GO, FLUX and COSY tariffs – if your dwelling has a domestic battery and solar PV panels. My hope is that it will help people make rational choices about which option is best for them.

Download

The Excel spreadsheet can be downloaded from this link. If you are downloading this macro-enabled file on a Windows computer, then the macros will probably be blocked by default. To change this you may need to first close the file in Excel, then right-click on the file’s icon and select the ‘Properties’ pane. Here you should see a tick box labelled “unblock”. If you unblock the file then it should work correctly.

Please note, the spreadsheet comes with no guarantee of anything at all, and it can at times be very slow to re-calculate.

Structure

The spreadsheet consists of two parts.

• At the top are several boxes that set the parameters of the simulation, and which show the results. I think of this as a ‘dashboard’
• Below this are 8,760 rows, one for each hour of the year. The spreadsheet proceeds through the year hour-by-hour simulating the flows of electricity from solar PV panels and the grid, into and out of batteries and your dwelling.

Throughout the spreadsheet, cells which are ‘inputs’ i.e. cells that you might reasonably want to change, have a yellow background with red text. Cells which are ‘outputs’ i.e. which contain the results of calculations have a red background with white or yellow text.

Click on image for a larger version. The visual appearance of ‘inputs’ and ‘outputs’.

The Dashboard: Overview

Click on image for a larger version.

The dashboard has controls in four regions.

• Regions 1 & 2 are about the tariff being simulated and the prices of the tariffs in each of their cheap, medium and high periods.
• Region 3 sets the parameters of the battery and the charging strategy, the amount of solar PV generation, and the likely household load.
• Region 4 contains the results of the simulation.

The Dashboard: Tariff Section

Click on image for a larger version.

After selecting the tariff to be investigated in the drop down menu, the selected tariff will appear in the box on the far right.

The prices of the different tariff stages can be changed if you desire.

The timings of the tariffs cannot be changed on the spreadsheet. They are set in a Visual Basic macro with the code below. If you understand this sort of thing then you can see how you could adapt the spreadsheet to a different set of tariff timings.

Click on image for a larger version.

The Dashboard: Main Settings

Click on image for a larger version.

The settings for the simulation have four main parts.

The Battery Settings are as follows

• Battery capacity describes the amount of electrical energy the battery can store.
• Round trip efficiency accounts for the energy lost as electricity is stored in the battery and then later drawn from the battery.
• Charge Rate is the rate at which battery charges. This limits how much electricity can be stored in the battery over a given period.
• The initial state of charge is the assumed state of charge at midnight on the 1st January.

Click on image for a larger version.

The cells referring to summer and winter should really be in the neighbouring box about charging strategy [Edit: they have now been moved]. Why are they there? For some systems, it can be sensible to switch between different charging strategies through the year, depending on the amount of solar energy available. For my own system:

• In winter I charge the battery at night using cheap rate electricity and the battery then runs down during the day.
• In summer, there is no need to charge at night because the battery is charged for free during the day by solar PV electricity.

The two boxes allow one to choose the days of the year on which to switch strategy.

In the ‘Grid Charging Strategy‘ box one can choose between charging when electricity is cheap, and/or charging in the hour before electricity becomes expensive. Either option can be chosen to be active in summer, winter or both.

In the solar factor box, one can set the expected amount of solar generation expected. This is generated by scaling the hour-by-hour solar data by the factor shown in the box.

Note that selecting a target amount will give an accurate result if the average solar generation from 2005 to 2016 is selected in the drop down menu at the bottom of the box. If one selects solar data from a particular year then the amount of generation will vary in line with that years variable output.

Click on image for a larger version.

The final box simulates how electricity demand from the household changes through the year.

Click on Image for a larger version.

Demand consists of two components: a steady consumption every day, and a component which peaks in mid-winter simulating the use of a heat pump.

The length of winter depends on a setting from 1 to 5 as shown in the figure below.

Click on Image for a larger version.

How the simulation works.

Each row of the spreadsheet from row 48 downwards calculates the state of charge of the battery, the household demand, and the solar PV according to the settings on the dashboard.

The simulation works row-by-row proceeding hour-by-hour through the year. The columns are as follows

• A: Index
• B: The day of the year expressed as a decimal day.
• C: The hour of the day, used for calculating the appropriate tariff
• D: The season of the year WINTER or SUMMER according to dashboard settings
• E: The Tariff Rate (cheap, medium, or high) determined by the Visual Basic code described above.
• F: G: & H: The hourly background & variable consumption – and their total.
• I: The daily average of the demand
• J: The time of day expressed as a fraction of a whole day
• K: Blank
• L: The 3-day running average of solar generation (used for plotting)
• M: The hour-by-hour solar data selected in the drop down menu in the SOLAR box. This is looked up from the data table in columns AE to AQ
• N: Modified demand: this is the difference between current demand and the amount of solar PV currently being generated.
• O: The amount of energy delivered to the battery (if positive) or drawn from the battery (if negative). This is calculated according to the current state of charge of the battery.
• P: The amount of energy drawn from the grid (if positive) or sent to the grid (if negative). This is calculated according to the current state of charge of the battery.
• Q, R, S, & T : Imports: If column P indicates that electricity has been imported, columns R, S and T list the amount of it in each tariff charging rate: these columns are then totalised at the top to calculate the annual amount to be paid.
• U, V, W, & X : Exports: If column P indicates that electricity has been exported, columns U, W and X list the amount of it in each tariff charging rate: these columns are then totalised at the top to calculate the annual amount of revenue accrued.
• Y: Hourly cost of grid imports
• Z: Hourly amount due from grid exports
• AA: Depending on the charging strategy this is when the battery charges from the grid. Household consumption is also met by the grid during these periods.
• AB: The amount of electrical energy sent to the battery.
• AC: The current state of charge of the battery.
• AD: Blank
• AE to AQ. Hourly solar data for the years 2005 to 2012 downloaded for the EU Sunshine database for my 4,000 kWh/year solar system in Teddington.

Graphs

Click on Image for a larger version.

Scrolling down  the spreadsheet will show four graphs which can be helpful in understanding what is happening.

The first graph shows three quantities charted across each day of the year:

• The 3-day average of the selected solar data
• The daily household demand
• The capacity of the battery

The second graph shows the state of charge of the battery charted across each day of the year.

The third and fourth graphs show the state of charge of the battery during a typical summer and a typical winter day.

More Tariff Calculations: GO, FLUX, COSY and more

Friends, in the previous article I estimated the annual cost of running a household with various “time-of-use” (TOU) tariffs i.e. bargains with the electricity company in which the price of a unit of electricity varies through the day.

The article seemed to strike a chord with many people asking several “But what if..?” questions. This article is an attempt to answer some of those questions.

The reason that I think this is an important issue as TOU tariffs are likely to become more popular.

Why? Because getting people to avoid consuming during peak hours is cheaper and greener than building a new power station to meet that demand. It’s also cheaper and greener than using gas in an existing power station to meet that peak demand.

And as domestic batteries and solar PV installations become ever more common, avoiding consumption at peak times has become more achievable for ‘ordinary’ people.

But it has also become all but impossible to work out which TOU tariff will be cheaper (or greener). And this puts us in danger of perpetuating the confusopoly that currently exists in energy tariffs.

The only way I know to work out which tariff is cheaper is to simulate an entire year hour-by-hour with realistic consumption, solar PV and battery storage. And that is exactly what I did: please read the previous article if you want to familiarise yourself with that.

Click on image for a larger version. The concept of a Confusopoly was invented by Scott Adams.

When I began to write this article, I planned to look at

• Different Tariffs (Octopus’s GO, COSY, and FLUX tariffs – alongside the standard variable tariff)
• The effect of solar generation
• The effect of battery storage capacity
• The effect of a small (10 kWh/day) or a large (20 kWh/day) domestic consumption
• Seasonal Strategies

However as I began this program of work I realised I had bitten off more than I could chew.

So below I will look at some of these issues, but people’s requirements are so specific that I thought the best way to be helpful was to make the spreadsheet easier to use and that is what I have tried to do.

The Excel spreadsheet can be downloaded from this link. If you are downloading this macro-enabled file on a Windows computer, then the macros will probably be blocked by default. To change this you may need to right-click on the file and select the ‘Properties’ pane. Here you should see a tick box labelled “unblock”. If you unblock the file then it should work correctly. The next article will describe how the spreadsheet is structured.

One thing I won’t be looking at is the effect of charging electric vehicles. Sorry: I don’t have experience of this.

Tariffs

In this article I will investigate four tariffs: Octopus’s so-called COSY, GO, and FLUX tariffs, and a standard variable tariff (SVT) – currently (February 2023) set at 34p/kWh. The assumed costs and times of operation are shown in the figure below.

Click on Image for a larger version. Illustration of the variation in price through the day for electricity imports (left) and exports (right) on the Octopus Go FLUX and COSY tariffs. Also shown as a dotted line is current standard variable tariff (SVT).

The daily-average import prices of the FLUX and COSY tariffs are about 34p/kWh, the same as the SVT, but the average GO tariff is 37p/kWh.

The daily-average export prices of the FLUX and COSY tariffs are 22p/kWh and 15p/kWh, the same as the SVT, but the average GO export price is just 4p/kWh.

Solar PV and Demand

The standard demand that I used last time was based on my own home. It consisted of a baseload of 10 kWh/day and then a seasonally variable heating demand peaking at 15 kWh/day. The solar PV that I used previously was based on my own home generation of just under 4,000 kWh/year. But of course these are only relevant to me – and there are many combinations relevant to other people.

For example, the figure below shows four combinations of high and low demand and high and low solar generation. It’s clear that these are very different situations and that the likely cost savings will be very different in each scenario. And this does not include changing the size of the battery.

Click on Image for a larger version. Four scenarios showing the variation of daily demand and daily solar PV generation through the year. The upper graphs show high demand and the lower graphs show low demand. Solar PV generation varies from just under 2,000 kWh to just under 8,000 kWh. Also shown as a dotted line is 13.5 kWh of battery storage available in a Tesla Powerwall.

Pre-discussion

Before looking at any results it is important to understand the ways in which it is potentially possible to generate savings compared to the standard variable tariff (SVT).

• Using the battery alone it is possible to generate savings by avoiding peak tariffs.
• Using solar alone it is possible to generate savings by using solar electricity instead of drawing electricity from the grid. But solar PV doesn’t match demand minute-by-minute, sometimes oversupplying and sometimes undersupplying.
• Using battery and solar, can generate savings in several ways:
  • By avoiding peak and standard tariffs
  • By improved self-utilisation of solar electricity
  • By exporting electricity.

Bearing these factors in mind, let’s look at some results.

Charging Strategies with a 13.5 kWh Battery: No Solar PV

I compared GO, COSY and FLUX to the SVT without considering any solar PV generation, but instead I used four different charging strategies:

1. No strategy – i.e. the battery is not used at all.
2. Pre-charge the battery for an hour before the peak rate.
3. Charge the battery as much as possible in the cheap rates
4. Pre-Charge the battery before peak rate AND charge the battery during cheap rate.

The SVT for this scenario came to £1,849/year and the four strategies above differed in cost as shown below:

Click on Image for a larger version. Annual savings or extra costs compared to the £1,849 SVT when using different tariffs. This chart shows the impact of using a battery alone with no solar PV. See text for details.

• Not using the battery with COSY and GO costs several hundred more pounds per year. So don’t use one of these tariff’s if you don’t have a battery or solar!
• Pre-charging for one hour before the peak rate resulted in modest savings (~7%) compared with the SVT.
• Charging during cheap rate resulted in big savings on the GO tariff (-39%), modest savings on the FLUX tariff(-20%), but a significant increase on the COSY tariff (+17%).
• Pre-charging and Cheap Rate charging together saves a lot of money with GO, only a little (12%) with FLUX and is more expensive with COSY.

At first I thought the simulation must be in error to for COSY to cost more than SVT. But looking at the charging details I saw that in summer (when demand was lower) using the ‘charge when cheap’ rule led to overcharging on COSY because it has two cheap rates just 3 hours apart. This could be avoided with smarter programming.

My conclusion from this is that the only way to significantly save money when using a battery alone is with a tariff such as GO which offers very low prices (12p/kWh) and a large battery able to store almost a whole day’s consumption.

Charging Strategies with modest solar PV and two sizes of batteries

Next I considered a situation with a modest solar PV installation (~2000 kWh/year corresponding to ~ 6 south-facing panels) and either a small (5 kWh) or a large (13.5 kWh) battery. I then compared the different charging strategies I used in the previous section with each of the GO, COSY and FLUX tariffs to the SVT with no solar PV.

Click on Image for a larger version. Annual savings or extra costs compared to the £1,849 SVT when using different tariffs. This chart shows the impact of using a small battery (left) or large battery (right) with a small solar PV installation. See text for details.

Comparing these results with the previous ‘No Solar’ results, it is immediately obvious that – with or without a battery – even a modest solar PV installation saves a serious amount of money. The average saving from the ‘No Battery’ scenario is 33% compared to the SVT.

Pre-charging from the grid for one hour before peak rate results in extra savings on all tariffs averaging 41% independent of battery size.

Moving to a larger battery (13.5 kWh vs 5 kWh) is only really of significant benefit on the GO tariff (56% vs. 33%). For the FLUX tariff the extra battery capacity increases savings only from 33% to 42%.

Annual Variability

Next I considered the effect of annual variability. I estimated the annual cost for a scenario similar to that in my home using the GO and FLUX tariffs and solar data for each year from 2005 to 2016.

Click on Image for a larger version. Top: Annual variability in solar PV generation (kWh/year). Middle: Annual variability in estimated year cost using the GO tariff. Bottom: Annual variability in estimated year cost using the FLUX tariff. See text for details.

The year-to-year variation in solar PV yield was roughly ±10% over the years 2005 to 2016. Unsurprisingly, good solar years led to lower overall costs on both GO and FLUX and vice versa.

But there is a curious feature in the calculations. Using the FLUX tariff the average cost over the individual years 2005 to 2016 was similar to the cost calculated using the average solar data. But for the GO tariff this was not the case. Instead, the average cost over the individual years 2005 to 2016 was much higher than the cost calculated using the average solar data.

I am not sure why this is, but it may be a feature of the fact that the averaged solar data has less variability than data for any individual year.

Other scenarios 

At this point I realised that I was becoming overwhelmed. 

I realised that there was no way to systematically summarise the results of using different tariffs across all the possibilities of demand, solar PV, tariff and charging strategy. So rather than trying to calculate everything myself, I resolved to tidy up the spreadsheet and make it reasonably suitable for other people to use. The dashboard of the revised Version 3 is shown below. In the next article I will describe how to fill out the spreadsheet.

The Excel spreadsheet can be downloaded from this link. If you are downloading this macro-enabled file on a Windows computer, then the macros will probably be blocked by default. To change this you may need to right-click on the file and select the ‘Properties’ pane. Here you should see a tick box labelled “unblock”. If you unblock the file then it should work correctly.

Click on Image for a larger version. Image of the dashboard from the revised spreadsheet for evaluating the costs of different tariffs with different demand, generation, and storage scenarios.

Summary

I compared GO, COSY and FLUX tariffs to the SVT in a couple of scenarios. My conclusions are that:

• Using a battery alone is only really valuable if one uses a big battery to download very cheap electricity.
• However, even modest amounts (e.g. 2,000 kWh) of Solar PV can be very beneficial, and a battery increases the savings possible still further.
• Because the average solar data has less variability than the solar data from any individual year, some tariff’s may not generate a typical cost when using the averaged solar data.

Beyond this, I’m afraid you will need to do these calculations for yourself. More details in the next article…

 

Tariff Calculations: Octopus GO versus Octopus FLUX

Friends, I have struggled to write this article. As you may have noticed, it has taken weeks.

I started writing after I was asked on Twitter about a new electricity tariff called ‘FLUX’ offered by Octopus Energy. Would it be cheaper or more expensive than their ‘GO’ tariff?

It’s a simple question and one that is worth asking. But it is very hard to answer because it involves both hourly details, but also seasonal changes.

I could see how to get at an answer but I have struggled with my waning technical skills. Imagine if you will, an old boxer going in for one fight too many. Finding themselves on the ropes, they face the unavoidable and inevitable reality of their own decline. But bravely they struggle and finish the fight bruised and defeated, but with their dignity in tact. Similarly I have found my prowess with Excel and Visual Basic to be much diminished, but I have somehow battled through.

GO and FLUX

The Octopus GO tariff which I currently use offers 4 hours of electricity for 7.5p/kWh between 00:30 and 04:30 each day. The rest of the time the cost is 40.75p/kWh. Exports of electricity are paid for at 4.1 p/kWh.

During winter, I buy electricity cheaply at night, and then use it during the day. For most of December and January, the battery could not supply the house for the whole day and I had to purchase electricity at full price for a few hours on those days. Overall, the average price I paid was around 12p/kWh in those two months.

Click on Image for a larger version. Illustration of the variation in price through the day for electricity imports (left) and exports (right) on the Octopus Go and Octopus FLUX tariffs.

The Octopus FLUX tariff is more complicated. It has a cheap rate in the night, but only for 3 hours 02:00 to 05:00 and not so very cheap (20.4 p/kWh). But it also has a more expensive rate (47.5 p/kWh) during peak demand hours from 16:00 to 19:00 each day. The rest of the time the cost is 34 p/kWh.

Initially FLUX looks much worse than GO, but the twist is that FLUX offers much higher rates for exporting electricity: 9.4 p/kWh, 22 p/kWh, 36.5 p/kWh for the cheap medium and high rates respectively. These figures should be compared with the miserly 4.1 p/kWh on the GO tariff.

There are so many variable quantities that I really had no idea which tariff would be cheaper. The results of my calculations appear obvious in retrospect, but that didn’t make the calculations any easier! My conclusions are that:

• For small solar PV installations (<~4,000 kWh/year), the big savings from using the night time electricity on GO outweigh the gains from exporting electricity at a good price.
• For large solar PV installations (>~6,000 kWh/year), this situation is reversed: The savings from using the night time electricity on GO are outweighed by the gains from exporting electricity on the FLUX tariff.
• For medium-sized solar PV installations, the two tariffs have similar costs.

Click on Image for a larger version. Estimates of the annual cost of electricity on the Octopus Go and Octopus FLUX tariffs as a function of the amount of solar generation. This applies to my household – see text for details – and assumes a 13.5 kWh storage battery. 

It turns out that, if you have the capability to export lots of solar PV, then the FLUX tariff could result in very low – and even negative – electricity bills. In retrospect, this is sort of obvious, but it was not obvious at all to me when I began.

But the spreadsheet I developed for the calculation allowed me to do the calculations for different sizes of battery and different amounts of solar PV generation, so I’ve investigated the matter a little more deeply below.

Sadly, because the spreadsheet is Macro-enabled, for security reasons I can’t link to it from this blog and many users wouldn’t be able to download it anyway. But if you really want a copy, please ask for a copy in the comments and I will send it to you somehow. But be warned that the spreadsheet is complicated and slooooow. On my computer it takes around 1 minute to evaluate the yearly calculation.

[Update: I think you can download the spreadsheet from this Dropbox Link]

Let me explain how I made the calculation and then I’ll discuss a few more details.

How to work out which tariff is cheaper

To answer this question I wrote a spreadsheet which modelled the electricity use in a household hour-by-hour for an entire year i.e. the spreadsheet has 365 x 24 = 8,760 rows.

For each hour of the year I estimated:

• The household demand: I modelled this as being the sum of a fixed amount each day (10 kWh/day) plus an amount used for heating that peaked in winter at 25 kWh/day.
• Solar PV: Using the EU sunshine database, I downloaded hour-by-hour sunshine data for my house location from 2005 to 2016, and then averaged this to give a typical solar generation year.
• I then worked out how to supply the household demand.
  • If Solar Power exceeded demand, then the excess was used to charge the battery, and if the battery was full, the excess was exported.
  • If Solar Power was less than demand, then the solar power offset the imported electricity.
  • During winter, the battery was fully charged during the cheap hours.
  • I estimated the battery to have a round-trip storage efficiency of 90%.

The spreadsheet and associated VB Macros took days to debug, but here are the results.

Household Demand

The modelled daily demand is shown below along with the EU sunshine database estimate of PV generation amounting to ~ 4,000 kWh/year. Basic electricity demand is ~ 10 kWh/day but peaks at 25 kWh/day in mid-winter due the heat pump, and amounts to ~ 5,000 kWh/year.

Click on Image for a larger version. Graph showing the modelled daily household demand throughout the year, and the 3-day average of solar generation. The solar data is the average of the years 2005 to 2012 estimated for my location and array size in Teddington.

The relationship between Solar PV supply and household demand is such that one needs to use two different strategies depending on the time of the year.

• In the Winter: the battery is charged using cheap rate electricity and discharges during the day – sometimes running out at night.
• In the Summer: there is no night time charging and the battery charges during the day and discharges during the night.

These two modes are illustrated in the graphs below.

The first graph shows a week in winter under the two different tariffs. The four hours of cheap electricity under the GO tariff allows the battery to charge to full, but the FLUX tariff only has three hours of cheap electricity so the battery only charges to around 10 kWh. The battery then discharges to run the household, and is partially supported by the weak solar generation, but typically runs out well before the end of the day.

Click on Image for a larger version. The state of charge of the battery through 7 days in winter. The upper graph shows the Octopus GO tariff which allows the battery to be fully re-charged each night. The lower graph shows the Octopus FLUX tariff which only has enough cheap hours to enable partial filling of the battery. The solar generation is also shown in yellow.

The second graph shows a week in summer. At this time of year, solar generation is enough to run the household and charge the battery during the day, with enough left over for export.

Click on Image for a larger version. The state of charge of the battery through 7 days in summer. Also shown is the solar generation is also in yellow and electricity exports in grey.

The switch between the summer and winter strategies is made on day 90 and day 270 – an arbitrary choice but one which corresponds roughly to the point where the 3-day average of solar exceeds the average household demand. The graph below shows the state of charge of the battery throughout the entire year on both tariffs.

Click on Image for a larger version. The state of charge of the battery through the entire year. The top graph shows the estimate for the GO tariff and the lower graph shows the estimate for the FLUX tariff.

Costs

The simulation runs hour-by-hour through the entire simulated year. For each hour, I estimated how much electricity was imported and exported, and then applied the appropriate tariff rate. This allowed me to summarise the situation for my home as below.

Click on Image for a larger version. Results of calculations of cost of running my household on (top) the GO tariff and (bottom) the FLUX tariff.

Both tariffs offer the possibility of running a home very cheaply: with annualised energy bills in the range £30/month to £50/month. However the GO tariff appears to be cheaper in this simulation £34/month compared with £50/month for FLUX.

The analysis shows why: being able to fill up with electricity at 7.5 p/kWh reduces the cost the electricity dramatically – £298/year versus £648/year. The improved rates for export on the FLUX tariff (£209/year versus £38/year) aren’t enough to make up for that.

Discussion#1: The effect of extra solar generation 

My conclusion is that for me, with my existing 3,800 kWh/year PV installation, the GO tariff is more economical.

But having recently had extra panels installed, this conclusion may not hold. The difference in annual cost between the two tariffs is ~£193 and the typical difference between the FLUX and GO export tariffs is ~ £0.18. So if the new system could export ~1,000 kWh more in summer, then the balance could easily shift.

And indeed, that is what the simulations show. Notice that for 8,000 kWh of generation the annual cost of electricity would be negative i.e. the house would be a bona fide power station!

Click on Image for a larger version. Lower graph: estimates of the annual cost of electricity on the Octopus GO and Octopus FLUX tariffs as a function of the amount of solar generation. Upper graphs: Details of how the the import costs and export and rewards vary on the FLUX tariff (left) and GO tariff (right). [NOTE: The original graph had an erroneous curve plotted. This was updated at 23:27 on 23/2/2023]

If the newly installed panels generate as much as I hope, then the annual generation may approach 6,000 kWh and in this case, the FLUX tariff would be marginally cheaper.

Discussion#2: The effect of battery size 

Whilst I was making these calculations, I thought it would also be interesting to look at the effect of battery size. For my home – with solar PV generation of ~3,800 kWh/year – the simulations suggest that bigger batteries are better – no news there – but that above roughly 10 kWh the additional savings are minimal.

Click on Image for a larger version. Lower graph: estimates of the annual cost of electricity on the Octopus GO and Octopus FLUX tariffs as a function of battery size with ~ 3,800 kWh of solar generation. Upper graph: Details of how the the import costs and export and rewards vary on the FLUX tariff (left) and GO tariff (right).

This is a relief to me. It means that as the batteries degrade, the system itself is likely to continue to perform well for many years.

Discussion#3: Strategy 

Friends, life is complicated enough without having to consider battery management strategy. Nonetheless, this is where we are!

Observant readers may have noticed that I made no specific efforts to avoid consuming energy at peak hours because it doesn’t happen very often. But if it could be done, then on the FLUX tariff, there would be a reduction in both costs and carbon emissions during these dirtiest hours of the day.

The problem for me is that I am not sure whether the occult Tesla logic which controls my battery, is smart enough to avoid using electricity at peak times. If it could achieve this, then on days when the battery might be expected to run out early, the system might preemptively charge in the middle of the day (at 34p/kWh) and so avoid consuming grid electricity during the peak hours when the equivalent electricity would cost 47.5p/kWh.

For a load of around 1 kW for 3 hours the potential saving would be around 45p/day which over 60 days of winter might amount to ~£27/year.

Errors and Mistakes

Friends, writing this article has been very difficult, and I must warn you although I have carried out many checks, I might easily have made some errors. Sorry. Please feel free to point them out to me when you spot them. The results appear to be about right for my own situation and so I have modest confidence that the errors are not too major.

But overall, despite the fact there are errors and mistakes, I think this spreadsheet offers a tool for evaluating the complex interaction of solar generation, battery storage, and time-of-use tariffs. I hope it helps.

More Fusion Delusion

Friends, long term followers of this blog will know that I am sceptical of the relevance of nuclear fusion research to our climate emergency.

Despite my scepticism, there seems to be no end of investors willing to bet billions on projects which will inevitably fail.

This article is about a company called Helion which has a ‘new way’ of doing fusion.

There are videos describing the process which are extremely convincing and at first I just didn’t know what to make of technique: it all sounded so clever.

But eventually I came across a YouTuber (Improbable Matter) with experience in the field, and he made the major weaknesses clear.

This article is about that one major flaw in Helion’s technique which makes it inevitable that they will fail. There are many other flaws in the Helion approach, but I am concentrating on this one major and unavoidable flaw. Why? Because the processes are complicated and I don’t want to get sidetracked.

The conventional approach to fusion

To understand the novelty of the Helion technique, I will first briefly describe the conventional approach to fusion.

• The conventional approach is to fuse deuterium (D) nuclei (1 proton and 1 neutron) with tritium (T) nuclei (1 proton and 2 neutron). This reaction is chosen because it is the easiest pair of nuclei to fuse. And it’s still very hard.
• The idea is to get the mixture of these nuclei very hot – around 100 million °C – and maintain the pressure on the mixture with a strong specially-shaped magnetic field. When the pressure and temperature are sufficiently great, the fusion process starts and energy is released.
• The reaction is written D + T → 4He + n . The energetic nucleus of 4He (pronounced as helium 4) stays in the plasma and heats the plasma.
• Because the neutron (n) has no electrical charge, it is not confined by the magnetic fields, and it leaves the plasma and is captured outside the reactor and used to generate heat which is used to generate electricity in a conventional steam turbine.
• The reaction would then run continuously with the fusion reaction maintaining the plasma temperature, and a continuous stream of neutrons providing heating.
• The neutron flux from this reaction damages just about everything near the reactor and induces radioactivity in most substances.

The Helion approach

In contrast with the conventional technique,

• Helion plan to fuse deuterium (D) nuclei (1 proton and 1 neutron) with 3-Helium (3He) nuclei (2 protons and 1 neutron) using the reaction D + 3He → 4He + H (corrected on 26.2.2023)
• This reaction is called aneutronic because it doesn’t produce a neutron. This is important because it means that – in principle – the entire apparatus will not become intensely radioactive.
• The Helion process is not steady state but instead involves episodic fusion reactions every second or so.
• Their plan is to start with a mixture of D + 3He in a plasma which is then rapidly compressed using changing magnetic fields to cause the plasma to heat which triggers the fusion.
• The heat of fusion then causes the plasma ball to expand against the compressing magnetic field, and as it expands in the magnetic field, it induces electrical currents directly in coils wrapped around the fusion chamber.
• The electrical current would be ‘harvested’ directly without the need for steam generation and a turbine plant.

Helion say they have demonstrated the feasibility of this with small scale plant, and are building ever larger prototypes.

Why it won’t work

There are large number of reasons why the Helion scheme will fail. Perhaps the first and most obvious is that it uses 3He as a fuel.

The ‘conventional’ approach to fusion involves the raw materials deuterium – which is common and found in sea water – and tritium – which barely occurs naturally on Earth. Obtaining tritium is a major challenge for conventional fusionistas, but it nothing compared to the challenges of making 3He.

Helium-3 is even rarer than tritium and some You Tubers (link) are even suggesting that interplanetary mining will be the source for helium-3. Please pause at this point and reflect on just how bonkers this is.

Helion do not suggest interplanetary mining. They suggest building a completely separate and thoroughly energy consuming nuclear plant to generate helium-3.

But the reason for failure to which I would like to draw your attention today concerns the basic nuclear reactions they hope to exploit.

The graph below – taken from Wikipedia – shows how the reaction rate of several different nuclear reactions vary with temperature.

Click on image for a larger version. Graph showing the relative reactivity of different fusion reactions. Note that at 200 million °C, and assuming equal concentrations, the D-D reaction is just as likely as the Helion reaction D-3He. And the D-T reaction is around 100 times more likely.

Helion have only managed to heat their plasma to 100 million °C so far, but they state that they will shortly achieve a staggering 200 million °C. This will be tough but let’s believe them for now.

Notice that the reactivity of a D-3He plasma is roughly equal to the reactivity of a D-D plasma, and both are around 100 times less reactive than a D-T plasma. So using D-3He to start a fusion reaction rather than D-T is like using damp kindling rather than dry kindling to try to start a fire – it just makes everything harder.

But Helion insist this sacrifice is worth it because their reaction is aneutronic, and the energy of the expanding fusing plasma can be captured electromagnetically.

But let’s imagine a 50/50 mix of D/3He which starts to fuse. As you can see, the D-D reaction rate is equal to the D-3He reaction rate. So if we start out with a 50-50 mix, after a short while there will be D-3He reactions and D-D reactions.

So after a short while – nanoseconds in practice – the original 50% mixture will contain the products of both D-D fusion and the D-3He fusion. And one of the products of D-D fusions is tritium, T.

The promotional video at the start of this article discusses this (starting at 13m 58s) and says that the tritium T will be captured in the exhaust at the end of the reaction and stored. However, that won’t happen!

Because the D-T reaction is 100 times more likely than the D-3He reaction, even a small amount of T in the reaction mixture begins to ‘steal’ D, lowering the D concentration, and emitting neutrons. And leaving the 3He with nothing to react with. After spending a fortune preparing the 3He – the majority of the fuel will be left unused after the reaction cycle!

Simulation

Helion propose that the nuclear reaction ‘should’ proceed as shown in the graph below.

Click on image for a larger version. Graph showing the expected relative concentration of species as the Helion reaction proceeds. Starting with a 50-50 mixture of D and 3He, these nuclei react and the amount 4He and H increases.

In this graph I altered the initial mix from 50-50 to 49.7-50.3 to allow the lines for 3He and D to show up separately. One can see that the D-3He fuel is consumed through the reaction and there are no neutrons produced.

However, this is just not what will happen. In fact – as discussed in the video – there are several reactions that can take place. Wikipedia helpfully summarises the reactions:

Click on image for a larger version. The four most prevalent fusion reactions. The bottom reaction is the one Helion wishes to focus on. However the D-D reaction produces both T, 3He, protons (H) and neutrons. And once there is T present in the reaction mix, the top reaction (D-T) will produce 4He and neutrons.

But what is not discussed in the video is the effect of the D-T fusion reaction which is around 100 times more likely than the D-3He reaction at 200 million °C.

Given all these reactions, it can be hard to anticipate exactly how things will proceed. But I have modelled all four reactions based on their approximate likelihood i.e. on the availability of the respective reactants and their reactivity as shown in the first figure. My spreadsheet is available here: Helion Fusion Simulation.

Inevitably the model is an approximation, but it is more realistic than the single-reaction Helion vision. Some example graphs are shown below.

Click on image for a larger version. Graph showing the expected relative concentration of species as a 3He-D mixture begins to fuse. The dotted green line shows the accumulated neutron dose. Notice that compared with the previous graph, less of the 3He is consumed, the D concentration falls rapidly, and the tritium concentration remains low but non-zero. See next graph for details.

Click on image for a larger version. Detail of the graph showing the tiny but critical concentration of tritium (T).

What we see in the above graphs is that the D-D reactions produce T which does not sit inertly in the mixture. Instead it ‘steals’ the remaining D in the mixture leaving the 3He substantially un-burned. There is also a very strong neutron dose: roughly 10% of the nuclear reactions produce a high energy neutron.

Reality

So in reality, the Helion approach will not be aneutronic. Their apparatus will become just as radioactive and be subject to just as much radiation damage as in any other fusion approach.

Also their very expensive 3He will remain unburned and need to be scavenged and separated from the ‘ashes’ of the reaction.

• If just the Helion reaction occurred, then in the time window shown in the graphs above, 75% of the 3He would be consumed.
• But when one considers all the other reactions, only a maximum of 35% is consumed – even if the initial D concentration is optimised.

So does all this mean that the Helion scheme won’t produce fusion? That it won’t work?

The Helion scheme begins with a plant for manufacturing 3He. This would be a massive complex proposal which would consume vast amounts of energy. It could only be justified if the Helion fusion process were somehow a straightforward way to generate even more vast amounts of energy at very low cost (aside from the 3He).

But the Helion fusion process is definitely not straightforward. It is not ‘aneutronic’ and D-T reactions will be a real problem for them.

And then one comes back to the even more basic problem of episodic nuclear fusion which I discussed in my previous article on laser fusion. That for a modest sized plant – say with 150 MW of electrical output – one would need to build an apparatus to withstand. an explosion of 0.1 tonnes of TNT once a second. Continuously. For 30 years. Really?

What is really going on?

Discussion of fusion as a viable option for future energy generation is a distraction from the urgent task at hand – to stop burning fossil fuels as rapidly as possible.

If holding out the illusion of a future magical technology delays climate action by even a year or two then it allows big oil, big gas, and big money in general, to reap extra profits.

So I urge you to ignore the siren calls of fusionistas. Ignore the talk of cheap and clean energy. Instead, close your ears and tie yourself to the mast of your boat, and sail on to a renewable future using truly miraculous technology such as solar and wind generation, technology which actually works.

The MyVaillant App: a review

Friends, regular readers will know that I love my heat pump, a Vaillant Arotherm plus model with a nominal maximum heating power of 5 kW.

But regular readers will also know that I have been very disappointed with the software and controls for the heat pump. Back in October 2022 I wrote:

Vaillant Arotherm Plus Heat Pump: The good, the bad and the ugly.

In that article the “good” referred to the mechanical and electrical operation of the heat pump; the “bad” referred to the mysterious absence of a user manual; and the “ugly” referred to the Vaillant ‘sensoApp‘ used to control a few of the functions.

Recently Vaillant have released a new MyVaillant app to replace the sensoApp and I was eager to try out it. Could it be the elegant swan that grew up from the ugly duckling of the sensoApp.

In case you don’t have time to read this finely-crafted article, here is a summary of my findings: the MyVaillant app is a big improvement, but the operational data it provides is – as best I can tell – still just as inaccurate as it was previously.

Overview

After logging on initially I was mildly impressed, but then the next time I opened the app, I was asked me to log on again: select my country location, e-mail and password. This has happened several times since and I have been told over that Vaillant are working on this. I won’t mention it again, but it is a sign of poor testing.

After eventually logging on one is faced with a pleasing simple ‘Home Screen’. A glowing green circle displays the current set point temperature with the actual temperature below it – these numbers change in increments of 0.5 °C. The glowing circle changes colour from time to time, but I have no idea why!

Plus and minus buttons allow the set point to be easily adjusted. Clicking on these brings up a dialogue box which asks how long to change the set point for. After the chosen period – default is 3 hours – the set point will return to it’s previous setting or programmed value.

Click on image for a larger version. The Home Screen of the MyVaillant App.

The Home Screen contains buttons which link to four more important screens.

Click on image for a larger version. The Home Screen of the MyVaillant App and the screens to which it links directly.

These screens (see-above) allow access to the basic controls. It’s nice to see that ‘Activate Hot Water Boost’ – the most common reason I need the app – is just one touch away from the Home Screen.

Perhaps the most important screens are those for planning the weekly cycles for (a) heating and (b) domestic hot water. These are – in my opinion – textbook good design.

Click on image for a larger version. The screens for adding an  additional regular period of domestic hot water heating. Notice that one days settings can be copied and pasted onto another day.

So the app is well-structured, pleasant to look at and easy to use. A big improvement.

System Performance

Even bigger improvements have been made to the screens showing the system performance. An example screen is shown below for the week beginning 23 January 2023.

Click on image for a larger version. Example page show energy information for the heat pump during the week beginning 23 January 2023.The screen is shown left and on the right the screen is annotated to show how the various quantities relate to one another.

The display page shows:

• A: The electrical energy used to operate the heat pump – in this case 125.9 kWh
• B: The thermal energy captured from the air – in this case 230.1 kWh
• C: The thermal energy delivered to heat the house – in this case 332.1 kWh
• D: The thermal energy delivered to heat hot water – in this case 23.9 kWh

From these quantities the app calculates the Coefficient of Performance (COP) which it calls as Energy Efficiency.

If one touches any of the small graphs, a more detailed version is shown.

Click on image for a larger version. Clicking on the small energy graphs shows more detailed versions.

This display structure has been well thought through and is well executed. I would wish that the data could be downloaded, but this presentation is basically excellent.

However sadly the performance data shown is not accurate.

Accuracy

The MyVaillant app warns people that it is not accurate. But I think that despite this warning, in the absence of any other information, most people will take these figures at face value.

Click on image for a larger version. This warning screen appears before one sees the energy information pages. I recommend that one does not click the box asking not to show the message again. This should remind one that the data can be significantly in error.

Please note: Energy consumptions, energy yields and efficiencies are extrapolated based on various parameters. The actual figures may differ substantially in some cases.

Fortunately I have a monitoring system which measures the electrical consumption by the heat pump and the heat output of the heat pump. This allows a direct comparison between the app’s estimates and a measurement system which is certified to be suitable for billing.

So for the week illustrated in the figures above, the actual figures are shown in the table below.

Click on image for a larger version. Table showing the MyVaillant App estimates for electricity consumed and heat produced together with the measurements of these quantities by billing-grade instruments.

The MyVaillant estimates are seriously in error.

• The estimate of the electricity consumed is in error by 9.3%.
• The estimate of the heat produced is in error by 22%

Consequently, the estimate of the COP is seriously in error.

For the week in question, the average temperature was 4.0 °C and the minimum temperature was -5.1 °C, a cold week by London standards. A COP of 3.3 in such a week is quite respectable. A COP of 2.8 is not so great, and might lead someone to search for system improvements which would be illusory.

Click on image for a larger version. Temperatures in my back garden during the week in question.

The real problem with these errors is that the erroneous estimates are completely plausible.

Summary 

Assuming that Vaillant sort out the problem logging on to their app, then this app represents a really significant improvement over their previous offering. To all the engineers who have worked on this I would like to say: Thank you.

But the inaccuracy of the reported quantities is significant and I feel that if Vaillant cannot improve these estimates, then they should be indelibly marked as ‘indicative’.

Kettle versus Qooker

Friends, have you ever spent time with someone who has just had a Qooker installed?

Discussing topics even tangentially related to the heating of water will result in a torrent of gushing hot water praise for this life-changing water-heating innovation.

And somewhere in the gushing torrent will typically be claims that a Qooker is more energy efficient than heating water using a kettle. Having carried out extensive studies of the boiling of water in domestic settings, I was sceptical. So I asked the oracle that is OpenAI’s ChatGPT about the pros and cons of using a Qooker and using a kettle.

Click image for a larger version. Chat GPT thinks that a Qooker is more efficient than a kettle.

ChatGPT was of the opinion that using a Qooker was more energy efficient than using a kettle. But then ChatGPT is truth-agnostic:

Click image for a larger version. Chat GPT admits it cannot distinguish between ‘true facts’ and ‘false facts’.

So I thought I would make a calculation, and that is what this article is about. In case you don’t have the time to read the whole article, my conclusion is that there is not generally much difference in energy efficiency terms.

The actual answer depends on how much boiling water you use each day, and how much extra water you leave in the kettle each time you boil it. Yes, it’s that tedious.

I think there are a wide range of use cases where a Qooker might well be more energy efficient than a kettle. However, what Qookers actually save is time, and I think that is why people who own them like them so much.

What is a Qooker?

A Qooker is a device that preheats around 3 litres of water to just over 100 °C and holds it in a pressurised, insulated container – like a vacuum flask – under a kitchen countertop. Other brands of water-heating tap are available.

Click image for a larger version. Illustration of a Qooker with publicity photograph .

When boiling water is required – such as for making tea or coffee, or filling a saucepan – water at around 100 °C can be dispensed immediately via safety-tap.

Energy Efficiency

The energy-saving potential arises from the fact that the tap dispenses just the amount of hot water required. This is in contrast with a kettle which usually requires some amount of extra water be boiled each time boiling water is required.

However, in order to realise this benefit, the Qooker has to keep around 3 litres of water in a pressurised container at around 108 °C – and some of that heat leaks out constantly into the kitchen.

So the question to answer is the relative magnitude of these heat losses (boils extra water versus losing heat 24/7). I decided to write a spreadsheet. Obviously I asked CHatGPT to do this first but the result wasn’t very helpful.

Click image for a larger version. Chat GPT’s suggested spreadsheet  didn’t take account of the fact that kettles and Qookers waste energy in different ways: one by keeping water hot for an extra time, the other by heating extra water.

So I wrote my own spreadsheet. You can download it here and it’s key features are shown in the image below.

Click image for a larger version. It calculates the extra costs of using excess water and compares then with the constant losses from teh Qooker.

My conclusion is that while it is possible to use a conventional kettle more efficiently than a Qooker, in most common circumstances, the Qooker is likely to be more efficient.

For the scenario illustrated above – boiling 10 cups of water for tea/coffee and preparing a one litre saucepan of boiling water – both Qooker and kettle use 0.42 kWh to heat the water, but the Qooker ‘wastes’  0.24 kWh/day keeping the water hot and and the kettle ‘wastes’ 0.35 kWh/day boiling extra water.

Over a year the saving of 0.11 kWh/day would add up to a saving of around 40 kWh/year, around 10 kg CO2/year, and around £14/year. The financial saving on a £1,000 + investment is negligible, and in carbon terms (and financial terms) the money would be much better spent on insulation!

Which raises the question…

Why do people love their Qookers?

People love their Qookers. Their relationships can be almost as profound as their relationship with their Air Fryers. And as far as I can tell, the reason is that aside from their emotional investment in their tap, the Qooker saves time.

Think about the difference between using a computer where opening a file takes many seconds – and the windows are slow to refresh. One learns to live with such computers, but after one has used a faster computer, returning to using the old computer seems painful.

Similarly, I think this sense of instant availability can feel magical after a lifetime of waiting for the kettle to boil. Two minutes per cup of tea; ten minutes per day; an hour a week; two days a year; a big fraction of person’s life could be taken up waiting for the kettle to boil.

People’s devotion is nothing to do with energy saving, and certainly nothing to do with cost savings – which are all offset by the need to regularly replace filters.

So will I be getting one? No. It’s just one more thing I don’t need.

Notes

Friends, I ignored lots of things in this article.

• I ignored the fact that the lost heating energy isn’t really ‘wasted’ for either kettles or Qookers: it all goes into heating the house.
• I ignored the mass of the kettle which must be re-heated each time the kettle is boiled.
• And I ignored embodied carbon dioxide.
• And I ignored safety concerns. 

 

Appalling error by Octopus Energy.

Update: Within 24 hours of publishing this, Octopus sorted out the issue. Of course it should never have happened but mistakes do happen and I am grateful that they (eventually) sorted it out.

Apparently the error arose from a firm ware update to the smart meter which clashed with a meter reading, and Octopus’s system didn’t catch the error. 

Worryingly, several other readers of this blog and followers on Twitter have reported the same problem at exactly the same time. Anyway. I am older and wiser!

Friends, I am a customer of Octopus Energy who seem like a decent company with an agenda to push forward the energy transition.

But everyone makes mistakes. And I have just been sent a bill for a single month which is in error by more than £1,600. Yes, it is approximately one thousand six hundred pounds too much on a bill expected to be around £100.

This is an obvious error. Octopus’s own numbers do not add up. And yet they have still billed me for this incorrect amount.

I am sure this will be corrected eventually but I still feel frightened simply to see that amount indicated as an amount that I owe. Indeed, I am shaking as I type this.

The important question here is not my liability: it is this: If Octopus can make such a gross error and not notice it, how can we have have confidence that they – and other energy companies – are not making errors routinely but at a level at which people just might not notice?

What’s the problem?

Here are some excerpts from my bill for 12th December 2022 to 11th January 2023.

Click on Image for a larger version. Excerpt from Octopus Energy bill claiming that I used £1,873.30 of electricity between 12th December 2022 and 11th January 2023.

The excerpt above compellingly suggests that I owe Octopus energy £1,873.30 for electricity used between 12th December 2022 and 11th January 2023.

The excerpt below emphasises this. It shows the breakdown of that consumption between peak and off-peak hours. Most of the consumption arose because I apparently consumed 4,458.6 kWh of electricity during peak hours during this single month.

To understand how ridiculous that is, last year I used 3,323 kWh for the whole year! It’s an obvious error – but it gets worse.

Click on Image for a larger version. Excerpt from Octopus Energy bill suggesting that I used 4,458.6 kWh of electricity during peak hours between 12th December 2022 and 11th January 2023. For comparison , last year I used 3,323 kWh for the whole year!

Pleasingly, Octopus send a detailed breakdown of electricity showing consumption every 30 minutes during the entire month. the 31 pages each look something like the page below:

Click on Image for a larger version. Octopus send details of energy consumption for every 30-minutes throughout the month. Here is the page showing consumption on 12th December 2022.

From this you can see that on 12th December 2022, we downloaded energy to charge our battery using off-peak electricity. But on this very cold day, the battery had discharged by around 2:00 p.m. and we then consumed full price electricity for the rest of the day.

This cold day involved a relatively high consumption of electricity – 37.4 kWh/day – to operate the heat pump. But even if we did this every day it would still only amount to only 1,160 kWh for the whole month.

I went through each page and added up the energy used: It looks like this:

Click on Image for a larger version. Adding up the amount of electricity consumed through the month teh answer comes to 750 kWh at a cost of £104.94 – an average cost per unit of around 14.0 p/kWh.

Looking at the days individually and adding up the cost on each day, Octopus’s analysis suggest that I used around 750 kWh of energy in this period, or around 24 kWh/day. This is very much in line with what my manual meter readings suggested and what I was expecting.

It is not in agreement with the statement on the bill that I used 4,458.6 kWh of electricity during this period at a cost of £1,730. So Octopus’s own bill simply does not add up.

Octopus Response

I wrote to Octopus on 13th January pointing out this error and a representative replied to tell me that Octopus were aware of the issue and I would not be billed.

However it is now 24th January and I now have been billed! I am now alarmed and distressed.

Click on Image for a larger version. Octopus told me they were aware of the issue and I would not be billed.

How has this come about?

I think I know how this has occurred. I think it arises from a single erroneous reading of my meter.

I subscribe for £12 a year to the Powershaper service which gives me access to my own half-hourly electricity readings! I think these data are read from the same database used by the electricity companies for billing.

Looking through these readings I noticed an anomaly on 11th January 2023. In a single half hour between 7:00 and 7:30 a.m. I apparently used 4,295 kWh.

This is more than my entire annual usage last year in a single half-hour period! It corresponds to electricity consumption at a rate of 8.59 MW – megawatts! – for the entire half hour. This is not physically possible, and is obviously an erroneous reading.

Click on Image for a larger version. Octopus told me they were aware of the issue and I would not be billed.

I think Octopus have overridden their automated systems to correct this error on the day-by-day analysis, but failed to correct the consequental error on the monthly bill.

So how can Octopus have made such an error?

I just don’t know how sophisticated billing systems which must surely be audited (?) could possibly make such an error.

I am sure that Octopus will eventually correct this. But I am frankly appalled that it is even technically possible to issue a bill which is so grossly in error.

So check your bills!

Carbon Dioxide Accounting: Why I hate it.

Friends, the turn of the year is the time when Carbonistas such as myself look at their carbon dioxide accounts. Like all accounting it is tedious but sort of important.

However carbon dioxide accounting depresses me more than regular accounting because I can hardly believe any of the numbers!

Allow me to explain…

The Big Picture

Click on image for a larger version. The red line on the graph shows estimated emissions from my household if I had not undertaken any refurbishment. The data are calculated month by month out to 2040. The green line shows actual estimated emissions from my household. The black dotted-line shows the additional effect of paying Climeworks to remove CO2 from the atmosphere on my behalf.

The aim of my activities and expenditure over the last three years has been to reduce ongoing carbon dioxide emissions from all aspects of my life, but targeting especially my home.

The graph above shows how I expect household emissions to accumulate based on various assumptions. Notice the scales: the horizontal scale extends out to 2040, my targeted date of death, and the vertical scale is in tonnes of carbon dioxide. Tonnes!

• The red line shows how I would expect emissions to accumulate if I had made no alterations to the house.
• The green line shows how I expect emissions to accumulate based on the current plan. This is based on the amount of electricity I draw from the grid.
• The dotted black line accounts for the activities of Climeworks who have promised to permanently remove 50 kg of CO2/month in my name. This line is dotted because I don’t personally have any evidence that Climeworks are actually removing CO2 from the atmosphere.

The net effect of my efforts will hopefully by 2040 amount to around 78 tonnes of CO2 emissions which do not take place. But in honesty, I am not very sure about these numbers.

Assumptions, Assumptions, Assumptions

Working out the data for this graph involves estimating the amount of electricity and gas that the household has consumed (not so hard) – and will consume in future (a bit harder, but still not crazily difficult).

However it also involves associating an amount of carbon dioxide with each unit of gas or electricity used – the so-called carbon intensity (CI) measured in kilograms of CO2 per kilowatt hour (kgCO2/kWh) of gas or electricity. And I genuinely do not know what numbers to use for these CI’s.

Allow me to explain my difficulty.

Assumptions for gas

For gas, a hypothetical 100% efficient boiler would produce around 0.18 kgCO2/kWh.

But it also takes energy – and thus emissions – to extract and deliver the gas to my boiler, and these emissions should also be associated with my consumption.

However, allocating these ‘up stream‘ emissions is not straightforward. It will differ depending on the source of gas e.g. from the North Sea (~+0.013 kgCO2/kWh) or liquified natural gas shipped from (say) the US (~+0.035 kgCO2/kWh). And also it will vary with the distance gas is pumped through the gas distribution network.

And then there is the giant smelly elephant in the room: leaks.

The gas network leaks. At every point from gas platforms to our homes, leaks are very significant. Probably around 1% of the gas we consume leaks, and some of the burned gas escapes without combustion.

When methane leaks it enters the atmosphere, staying for around 10 years before reacting to form CO2 and H2O . And during that 10 years or so, it warms the atmosphere much more intensively than CO2. Averaged over 20 years – methane is around 80 times more powerful greenhouse gas than CO2.

So a leak of 1% anywhere from the gas well to our homes increase the carbon intensity associated with methane by approximately  1% x 80 x 0.18 = 0.144 gCO2/kWh. Combined with upstream emissions this practically doubles the carbon intensity of burning methane compared with the value used by most web sites.

The only way to really know the amount CO2 emitted associated with gas use, is to use no gas at all: anything multiplied by zero is zero.

Assumptions for electricity

As difficult as it is to truly know the  appropriate carbon intensity (CI) to associate with gas consumption, it is much more difficult to know the appropriate CI to associate with electricity consumption. This is because electricity is generated from several different sources, each with its own characteristic CI.

For example, as I type this, this web site tells me that the carbon intensity of the electricity I am using is 0.065 kgCO2/kWh, but this web site tells me that the carbon intensity of the electricity I am using is 0.101 kgCO2/kWh. Which should I believe? I just don’t know.

Both figures will change depending on the composition of generating technologies, but they have (I suppose) made different assumptions about how to account for some emissions. I have previously written to the web sites to ask but received no reply.

Click on image for larger version. Data from MyGridGB and National Grid on carbon intensity. the two sites give answer which differ by 0.036 kgCO2/kWh – amounting to ~30% discrepancy.

But what if I want to draw some extra electricity? If I switch on a tumble dryer, this extra demand must be met by a source which can be switched on to meet that demand, and in practice, this is always gas-fired generation, which is nominally assigned a CI of 0.45 kgCO2/kWh.

So I have to choose whether to allocate an average CI (0.101 or 0.065 kgCO2/kWh) or a marginal CI (0.45 kgCO2/kWh) to my consumption. How do I decide what is my average consumption and what is marginal? I genuinely do not know.

And additionally, the same elephant (methane leaks) that was in the room for gas consumption, is still in the room for electricity derived from gas-fired power stations. Accounting for leaks, the contribution to the average CI of gas-fired generation could practically double from 0.45 kgCO2/kWh to 0.81 kgCO2/kWh which is almost as bad as coal-fired electricity generation.

And there are similar problems accounting for electricity exported from – say – solar panels. In principle, each extra kWh exported displaces a kWh that would have been generated by gas-fired generation. And so exports of solar electricity are avoiding emissions of CO2 at the marginal rate for gas-fired emissions (0.45 kgCO2/kWh). But should this also include the effect of methane leaks avoided?

And since the CI of grid electricity is changing all the time, should I do my accounting in (say) half-hour periods? Or should I use day or night averages? Or weekly, monthly or yearly averages?

Click on image for a larger version. graph showing the variation in CI with time of day: using electricity at night is generally a bit greener because the fraction of electricity generated by wind and nuclear power is greater. Data from the Carbon Intensity web site.

And some argue that the CI of grid electricity varies from region to region! They argue that in regions where there is lots of renewable generation the ‘local’ CI is low. But this ignores the fact that it is essentially a single grid, and that if these regions were isolated, the grid would not be able to function.

Click on image for a larger version. Map showing regional sub grids together with an indication (by colour) of the ‘local’ carbon intensity. Data from the Carbon Intensity web site.

So what do I do?

Friends, this is why I hate carbon accounting: just changing the accounting basis can apparently change emissions associated with electricity or gas consumption depending on where they take place, and how many leaks are associated with the consumption. And part of this is real, and part is conventional practice, which ignores critical issues like methane leaks.

So one can find oneself making spreadsheets of enormous complexity in search of an accounting accuracy that is ultimately unattainable.

So in the face of all this complexity and ambiguity I assign the same carbon intensity to gas and electricity (imports and exports) of 0.230 kgCO2/kWh.

• For gas this is a bit higher than higher than estimates that add upstream emissions but much lower than estimates that account for methane leaks.
• For electricity this is roughly the average CI for the years 2019 to 2022 as specified on the MyGridGB web site. If this figure changes significantly in 2023 I will update it.

Click on image for a larger version. Map showing carbon intensity averaged over one year showing the systematic reduction in CI. Data from the MygridGB web site.

The graph at the head of the article shows progress so far and how I anticipate things unfolding over the years. In calculating that graph I disregarded…

• Exports of solar electricity which could be considered to be avoiding emissions by displacing gas-fired generation.
• The share of a wind farm that I bought and which should start generating from November 2023. Again, this could be considered to be avoiding emissions by displacing gas-fired generation.

If add these in to my projections, (CI = 0.23 kgCO2/kWh) then the outlook looks better. However, the uncertainties in all the numbers here are so great that I just don’t know if any of it is correct. That’s why all the lines are dotted.

Overall, I know that household gas consumption is zero and therefore so are emissions, no matter what the CI. And this year I expect that we will be more or less off-grid i.e. taking no electricity from the grid – for roughly 6 months. And so I know emissions during that period will be zero. In short, just minimising grid consumption is probably the best way to ensure that associated carbon dioxide emissions are low.

Click on image for a larger version. The red line on the graph shows estimated emissions from my household if I had not undertaken any refurbishment. The data are calculated month by month out to 2040. The green dotted line shows estimated emissions from my household accounting for electricity exported in the summer as ‘negative emissions’ i.e. I have avoided someone else emitting CO2. The black dotted-line shows the additional effect of paying Climeworks to remove CO2 from the atmosphere on my behalf. The blue dotted-line shows the ‘negative emissions’ effect of shares in a wind farm due to begin generating in November 2023.

The Great Carbon Dioxide Accountant in the Sky

Friends, on the sacred slopes of Mauna Loa in Hawaii, there is a carbon dioxide accountant far greater than I.

Click on image for larger version. Mauna Loa CO2 observatory: the  location of the great Carbon Dioxide Accountant in the sky.

Patiently this accountant has been monitoring the concentration of carbon dioxide in the atmosphere since 1959, the year of my birth.

This accountant:

• Does not care about which value of carbon intensity I use in my calculations.
• Does not care about whether I used the correct estimate for embodied carbon in my solar panels or triple-glazing
• Cannot be sweet-talked with promises of future emissions reductions.

They just measure the concentration of carbon dioxide in the Earth’s atmosphere.

When the volcano is not erupting, this accountant publishes their results daily. And this global accountant shows that whatever we are doing is just not enough.

Even if this curve stabilised at its current value of around 420 ppm, the Earth would not cool. But this curve is not stabilising – it is still rising – and it is our actions that are causing this – and only our actions can stop it.

Click on image for larger version. Black Curve: Monthly average atmospheric carbon dioxide concentration versus time at Mauna Loa Observatory, Hawaii (20 °N, 156°W). Red Curve: Fossil fuel trend of a fixed fraction (57%) of the cumulative industrial emissions of CO₂ from fossil fuel combustion and cement production. This fraction was calculated from a least squares fit of the fossil fuel trend to the observation record. Data from Scripps CO₂ Program.

 

 

Gas and Gaslighting

Click on image for a larger version. BBC News stories detailing gas explosions this autumn: See end of article for links.

Friends, welcome to 2023.

I would have liked to start the year talking about something positive, but I can’t!

Over the Christmas break it struck me just how astonishing it is that we still allow homes to be heated by burning methane gas.

And we even build new homes incorporating this deadly and disgusting technology.

In case you didn’t know:

• Over 100 people a year in the UK die from carbon monoxide poisoning, mainly arising from poorly-maintained gas-burning equipment.

Click on image for a larger version. Graph showing data from the Office for National Statistics on the number of people killed each year from carbon monoxide poisoning (link).

• Even when gas-apparatus functions correctly, gas cookers emit toxic fumes into the homes of people who cook with gas. It is likely the highest exposure to mixed oxides of nitrogen (NOX) that you will experience anywhere in the UK is not by a roadside, but in a kitchen.

Click on image for a larger version. While cooking with gas in this US household, NO2 levels rose to almost 300 ppb. This figure is modified from the linked article.

• And on top of it all, every year gas causes more than 300 explosions in the UK, killing or maiming around 100 people each year.

Click on image for a larger version. There are over 300 fires involving gas and an explosion every year. About 100 of these incidents result in a casualty or a fatality (Data Source).

• And on top it all again, burning it emits tonnes of carbon dioxide, a gas which is destabilising the climate on which we depend.

So how is it that we tolerate such a technology? Why are we not outraged?

Gaslighting

Friends, we are being ‘gaslighted‘ by the Gas Industry.

Gaslighting – as Wikipedia puts it – is a term that:

…”may also be used to describe a person (a “gaslighter”) who presents a false narrative to another group or person, thereby leading them to doubt their perceptions and become misled, disoriented or distressed. Often this is for the gaslighter’s own benefit.

The gas industry – and the media it influences – suggest that the deaths and appalling climate impacts of burning gas are in some way ‘normal’ and ‘acceptable’.

Because we are familiar with gas, they propagate a false narrative that ‘burning gas’ is somehow ‘safe’, ‘natural’, ‘warming’ and ‘friendly’.

To understand how shocking and deceitful this really is, try the following exercises:

• Imagine that Wind Turbines killed more than 100 people a year.
• Imagine that Heat Pumps killed more than 100 people a year.
• Imagine that Solar Panels killed more than 100 people a year.

Do you think there would be media outrage? Of course there would! But with gas – these consequences are literally just ignored.

The Reality

The reality is this: gas is a filthy polluting technology and burning gas damages our climate, our health, and kills over 100 people a year in the UK alone, as well causing 300 explosive fires per year.

I urge you not to be misled into thinking that gas is anything other than a toxic mistake. If you can, I urge to eliminate gas appliances from your life.

BBC News Story Links

• 10 dead in Jersey
• Critical Injury in Evesham
• Barnsley Explosion
• Gas Canister Explosion in Glasgow
• Nottingham Gas explosion
• Four year-old girl killed in London
• Man dies in Scotland