Solar #7: Interim ROI Figures - 1st 6 months

Introduction

In a previous post I reviewed our solar and battery system after 6 months of use and, at that point, I was awaiting bills from Octopus Energy for the whole 6 months & so could not calculate the Return on Investment (ROI)...

 

...I now have the electricity bills covering the period 1st March to the end of September - a net payment to me of £157.84.

 

 July Import and Export estimates from the Octopus App.

Both graphs show £'s, but the vertical scales are different!

Note that the  Import Costs Don't include the Standing charge

(approx £0.5/day or £15 over the month)

 

But what does this mean in terms of an ROI:

• What do I expect the winter ROI to be?
• What was I paying before for electricity?
• What would I have paid for electricity without a solar system?
• Compare this ROI to what?

 

An important point: this post is just my views on investments and is not intended as investment advice.

 

ROI Comparison and Assumptions

For solar systems I often see 'payback period' quoted, eg 'investment of £7,000 paid back in 8 years from savings in electricity bills'. While this is interesting and makes a nice headline it is not very useful. If I have £7,000 am I better off investing my money in a solar system, or should I put it in a high interest savings account or what about an ISA and put the money in stocks and shares?

 

For me the most useful comparison is my pension (a SIPP). I am recently retired and withdrew £16,220 from my SIPP to invest in the solar system. Would I have been better leaving it in an investment fund (eg FTSE250 Tracker Fund or the more 'sporty' S&P 500 Index fund). There are some key differences that I have allowed for to make this comparison:

• I am told that my solar system will last for 25 years. In reality I am sure that it will not suddenly conk at exactly 25 years after installation, but for the model I assume that its value drops to £0 at 25 years. For an investment fund, while 'the value of an investment can go down as well as up', I might anticipate an average return of a few % each year and hope that my capital is intact after 25 years.
• The investment fund will not necessarily rise with inflation. I am now drawing down on my pension pot. My financial advisor modeled this to give me a regular income and the pot of money running out when I reach 99 years old. He modeled 2.5% inflation (ie my annual pension rise) over the period of my retirement - ie the average return of the funds that I am invested in needs to beat 2.5% just to stand still. In the case of the solar system then the return on investment rises as the price of electricity rises. In this model I assume that the price of electricity rises in line with inflation at 2.5% each year. There are more notes on inflation below.
• I paid for an 'in roof' installation for my solar panels. This added £2,573 to the price and gave us the benefit of a new half roof. However, my roof, while it is working well at present (ie it protects us from the Lancashire rain), it is, apparently, at the end of its 50 year design life. For me £2,500 was a necessary part of the solar investment, but you could argue that if I invested the money in a fund then I would be still be paying to replace the roof in the next 25 years. I have modeled with and without the 'in roof' cost.
• Investment fund costs: an investment fund manager (eg Legal and General) and the provider (eg Hargreaves Lansdown) will have charges - I have assumed 0.5% as being pretty typical total charge (ie fund + provider). The solar system has no equivalent charge.
• Inverter and battery replacement. I am told that inverters and batteries have a life of around 10 years. To make the maths easy I have assumed that at year 12 a new inverter & battery must be purchased. I have used current prices for direct replacements (£5,050, based on battery at £3,600 and inverter at £1,450). Of course there will be an installation charge, but I have assumed that this is wrapped up in the £3,600 battery option price that I was charged for the original installation.
• I still don't know how my solar investment will perform from October to March. Clearly much less sunshine. However, the battery lets me buy electricity at around 15p per kWh, that otherwise I would pay about 25p per kWh. So this saves me something like £1 per day (see my post Solar#6: 6 Months Post Install) or £180 for 6 months; on top of this there will be some solar production - even in Lancashire there will be some sunlight. In the end I took winter ROI to be 50% of summer ROI and have also modeled two cases of +£100 and -£100 to test the sensitivity.
• If I had put my £16,220 into an investment fund then I assume that I would have stayed with a regular tariff. I have found it hard to find alternative non-solar tariffs & so I have just used the mid-point tariff that I am charged with Octopus Flux. With no solar system I would also not need to pay for the electricity to power the inverter and battery. I have used our mid-point consumption over a year prior to the solar system being installed (10.18kWh per day). This has been reasonably static over the last 3 years (from 2021, 9.66kWh to 10.69 kWh daily average over a 12 month period). This is probably a little pessimistic for this summer analysis as we use a little less electricity in the summer, but should should come out in the wash over a full year.

 

See also my April 2024 investment case post - this has more on the risks of investment in solar.

 

Model Construction

My aim is to find what annual percentage growth I should look for in an investment fund to give an income that is equivalent to the electricity bill savings from my solar investment:

• Step #1: solar ROI over 12 months:
• Analyse my bills with solar Vs what I would have been paying with a regular tariff and no solar system - this gives a return on investment of £353.45 in Q2 and £328.39 in Q3 (Octopus change their rates each quarter, so the bills have neat cut offs at the start and end of each quarter). The total for 1st April to 30th September is £680.84.
• I added 50% to this for the winter to give me a complete year return on investment estimate of £1,021.
• Step #2: model equivalent hypothetical investment fund:
• Investment £16,220
• Each year:
• I take out £1,021 as income (eg to pay the higher electricity bills without solar power)
• And the fund grows by x%
• 25 rows in an Excel spreadsheet gives me the remaining capital after 25 years
• By trial and error I set "x"% (the annual growth rate) so that the remaining capital is £0 at 25 years (as per my assumed value of the solar system)
• Step #3: account for inflation and investment fund charges
• Once I have "x" (the equivalent annual growth), I then add:
• 0.5% to cover charges for the investment fund
• 2.5% to cover inflation
• Step #3: Test some variants of this model:
• At year 12 add £5,050 into the fund - what I would have paid to replace the battery and inverter mid-way through the life of the solar system
• Invest only £13,647 at the start, ie no re-roofing - 'on roof' solar system.
• Add and subtract £100 to the annual return on investment (ie test out £1,121 and £921 income each year) - the aim here is to test the sensitivity to differences in performance (eg errors in my winter estimates).

 

Results

My view is that the base case is the on-roof installation (ie £13,647 initial investment) and replacing the inverter and battery after 12 years. This gives an equivalent fund growth of 6.4%.

 

If I take the higher investment then the equivalent investment fund only needs to return 4.9%.

If my inverter and battery lasts the whole 25 years, with the initial investment of £13,647) and nothing added to the fund at year 12 then the fund would have to grow at 8.5% to match the solar system ROI.

If the annual return from the solar system is +/-£100 from my estimates (based on the 1st 6 months) then this adjusts the equivalent investment fund ROI by about +/-1.1%.

 

Discussion

This model includes a large number of assumptions. For some of these I have modeled the sensitivity and this gives a range of 3.8% to 9.6%. Others I have not modeled, eg removing inflation takes out 2.5% from the equivalent fund return, but it might also be reasonable to take 5% as an inflation rate for electricity (see below).

 

In the end 6.4% seems like a reasonable mid-point between the range of assumptions. I guess that for what is, I hope, a low risk investment, then perhaps this is OK.

 

I would also point out that, based on my experience, the extra work that comes with the battery (see post "6 Months Post Install" is far more than comes with a typical SIPP investment. Potentially a solar only installation, with no battery, would compare better for the work required.

 

In my April 2024 investment case post I used an annual return of £605 based on a solar system price of £7,000 based on figures from the Energy Savings Trust. Given that I invested £13,647, then I might expect a pro rata increase in return, ie £1,175 each year. I feel a post is need in April 2025, with 12 months figures to see if I am being overly pessimistic with my annual return estimate or over optimistic with my April 2024 estimates.

 

Notes on Inflation

Taking the last 25 years (1999 to 2024) then the average UK inflation rate has been 3.2% (ie £10,000 had the purchasing power of £21,719 in today's prices) - see https://www.officialdata.org/UK-inflation.

 

Why have I used 2.5%? It just happens to be the rate that my pension advisor use for modelling my income from my pension pot. So if the last 25 years is a good guide to the next 25 years then perhaps we should add 0.7% to the equivalent pension fund return needed to match the solar system return

 

But what about electricity price rises? If electricity prices rise at a slower rate then my future £ savings will be lower, conversely steep rises in electricity prices will mean that my investment in a solar system is getting a bigger return. Our new government (2024) is promising big investments in renewable energy and so reductions in domestic energy bills. However, over the last 25 years electricity prices have risen much faster than inflation, at 5.4% per year on average (See the report on "Domestic energy prices" from the House of Commons Library: "In April 2024 prices for gas were 270% above their January 2000 level in cash terms, and electricity prices were 350% higher").

 

My view is that inflation is all in the risks of investing in solar power - upside and downside (see Solar#2:Solar Panels Investment Case) & I will stick with 2.5% as the mid-point for inflation, and accept that it could go either way. The really good news is that the risks are very different to my more traditional pension investments and that having a range of different risks, for different pension investments, is often said to be good (see book "How to Fund The Life You Want" by Robin Powell and Jonathan Hollow).

Solar#6: 6 Months Post Install

Introduction

I kept putting off writing a 3 month review of our solar system - I want to write an honest appraisal and I had really wanted to be able to paint a 100% glowing picture of a fantastic investment...

...but, like most things, there is good and bad and the data is ambiguous. So, at last, I have bitten the bullet and written this 6 months review:

 

There are a few points that are not covered and I intend to cover in future posts:

• 6 months Return on Investment (ROI): as I write this, Octopus has resolved my smart meter issue and are just finalising my June through September bill. In this post I discuss energy (kWh) and power (kW), but in the end it all boils down to money (£)! A future post.
• Scheduling: GivEnergy are promising a solution to the firmware issue that, in some cases, means that scheduled discharging is not optimal - I do have a summary of this issue below and I will discuss the scheduling topic in more detail in a future post, once, I hope, the issue is resolved.
• Emergency Power Backup: I have not yet paid the extra for this facility to be installed.

 

I don't want this introduction to sound like I am disappointed with the system, so a few, more positive, points:

• It works - the system is robust and the 6 month performance looks to be on track to beat the 12 months figures in the Lovatts proposal
• GivEnergy software is cool and the overall hardware and installation looks neat, well designed and with solid construction.
• The GivEnergy software does allow the system to be scheduled for the Octopus Flux tariff and, so far, I believe it has been broadly optimal over the course of the spring and summer.

 

Lovatts Installation

All functioning well - solar system and roof.

Installation - My View: It Looks Good

Since the installation I have had not really needed any help from Lovatts and I would certainly recommend them to others. Their one shortfall is their limited knowledge of scheduling the GivEnergy system and they have tried to help here, but are limited by the support & documentation provided by GivEnergy.

 

GivEnergy Hardware

Not much to say here - it still looks good and has not skipped a beat.

 

GivEnergy Software - Phone App

This has great monitoring capabilities; this does help in understanding where energy is consumed and how to make savings - see section below "Consumption Visibility Gives Savings".

 

It took me a while to work out that the scheduling features of the phone app are limited; really a sub-set of the cloud app. Since working this out I have not used the phone app for scheduling. To add to the confusion:

 

• My installer had limited knowledge of how to schedule the system
• The GivEnergy helpdesk, while staffed by very nice and helpful people, do not have knowledge of the scheduling features of the system and they were not aware that the scheduling interface of the iOS app is different to the Android app version described in their documentation.
• The phone app documentation is limited: the more obvious features are fully covered, but not scheduling.

 

My view now is that the phone app (iOS and Android) are great as a monitoring tool, but it is best to do the scheduling in the cloud app. This is actually fine if, as I do, you want a set and forget system. It is easy to do a quick check on how the system is running on the phone app, but setting the schedule is a more considered activity, done (I had hoped) just once.

 

GivEnergy Software - Cloud App:

This has similar monitoring features as the phone app and much better scheduling capabilities. The cloud app also has some interesting reporting and data download capabilities that I have only made limited use of. There is no documentation for the cloud app (or none that I could find) and the helpdesk know very little about the scheduling features.

 

Scheduling with the Cloud App: Once I have the GivEnergy firmware bug fix I will report on this in more detail, in a future post. Right now my schedule makes use of the Octopus Flux cheap rate and peak rate periods:

• Timed charge: from 2am to 5am to 100% (Octopus Flux cheap rate)
• Time discharge 2 x 45  mins during the peak rate of 4pm to 7pm. The 2nd discharge finishes at 7pm. Both are set with a 4% minimum battery charge. My aim is to avoid consumption from the grid at peak rate and then to maximise discharge to the grid, without manual intervention.
• Outside of these times I use the ECO mode that works as you would want - ie prioritising home consumption of solar energy, then charging the battery and finally if home consumption is satisfied and, the battery is at 100% charge, it exports to the grid. If the available solar power is not enough for home consumption then the battery is used and, if this is exhausted, then the grid is used. If peak demand in the house exceeds the inverter/battery peak limit (nominally 5kW)  then the grid is used.

So far the battery has not been fully discharged at 7pm, on most evenings, ie we have only had very limited import from the grid at peak times and almost always because we have exceeded the peak capability of the inverter (nominally 5kW). This can happen, eg if the oven, grill and kettle are on at the same time, but actually accounts for very little energy (eg from my Octopus bill for 1st July to 28th Sept peak rate consumption is only 2.0kWh).

 

A bug in the GivEnergy firmware means that if we do not hit the 4pm peak rate window with the battery at 100% charge, and I do not adjust this time based discharge schedule, then we could end up with more peak rate import before 7pm. I think that this is likely to happen now that we are past the Autumn equinox.

 

Performance Numbers:

• Solar energy generation
• The Lovatt's proposals, based on the MCS calculation, states that for a 12 solar panel system we would produce 3,610 kWh each year. I was told to pro-rata this number for the 14 panel system, ie 4,212 kWh each year. After losses of 181 kWh/year (pro rated from the proposal), this gives 4,031 kWh
• For the 6 months from 1st April to the end of Sept the actual generation was 3,362 kWh. From what I can see the Solar generation energy figures given by the GivEnergy app are after losses, so 83% of the full year 4,031kWh in the proposal. It looks like we should exceed the proposal, by a decent margin, unless the winter is really, really gloomy!

 

• Power: The peak power of each solar panel is specified as 400 Watts in the proposal, or 5.6kW for an array of 14.  On 28th June the GivEnergy app showed a peak power generation of 7.257kW. I am a little dubious of this figure as on days with very variable levels of sunlight the peaks were always higher than on days with continuous sunshine. For example, the day with highest energy (36kWh) had a peak power of 5.38kW. I am not sure if this is an artifact of the measurement system of if the system is more efficient when there are only short bursts of high sunlight levels. In any case, given the non-optimal direction of the house, pitch of the roof and location on the globe, even the 5.38kW seems pretty good.

 

• System and Battery losses: I have struggled to make sense of losses - this is what I have worked out from the energy figures provided by the cloud app:
• Grid to home - there are no losses here (& you would not expect any, it does not go via the GivEnergy system).
• Solar: clearly there will be losses in the inverter, but the figures quoted are after losses, so it is not possible to work out (from the energy data) what these losses are.
• Battery in and out: This averaged 11% of the energy input or 1.28kWh per day over the 6 month period. During this time the battery was fully charged on most days, using cheap rate electricity and around 60% was discharged to the grid at peak rate with the remainder discharged to the house. On gloomier/shorter days there were one or more mini-discharge charge cycles. On longer sunnier days then the battery pretty much stayed charged and the house ran off solar/battery from 5am. While there is variability in the daily losses, there was no obvious relationship with the charge/discharge pattern on that day.

 

Solar production Seasonal Observations

Considering the 6 months from 1st April to 20th Sept (the first 6 months period where I have complete monthly data):

• Total production is 3,360 kWh, or on average 20.2kWh per day.
• The month with the highest production was June at 690kWh or, an average of 23kWh
• The month with the lowest production was September at 390kWh or, an average of 13kWh
• The June daily solar production range was 8.82kWh to 36.24kWh
• The September daily solar production range was 1.48kWh to 24.51kWh

It will be interesting to see how solar production fares in the winter months (as I make the final edit of this post, in mid-October, we have just had a miserable truly Lancashire day of  rain and dark grey clouds with only 0.76kWh of solar production for the whole day.

 

The thing that I have been surprised by is the enormous daily variability:

• Over 4:1 best day Vs worst day in the summer
• Over 16:1 around the autumn and spring equinox

Given this variability, then if a tariff requires scheduling to achieve maximum ROI (ie anything other than a flat rate tariff) then I think that the options are:

• Daily manual adjustments depending on expected weather and energy use profile
• A fixed schedule that is robust to a wide range of weather types and usage patterns
• A schedule that automatically adjusts depending on the weather forecast and expected usage patterns.

This topic is something for a future post - suffice it to say that my objective, at present, is the fixed robust schedule, & I think that, other than the bug mentioned above, the GivEnergy system allows this when combined with a tariff like Octopus Flux.

 

Accuracy: GivEnergy Vs Smart Meter Energy Readings

I took grid import and export readings roughly every 2 weeks from the smart meter and the GivEnergy software.  The readings for both import and export were in the range of 0.8% to 2.5% difference, with the smart meter giving slightly higher readings for both import and export. I was pleasantly surprised by how close these figures are and how closely the graphs match over complete days. The biggest deviations that I saw were in a period in September (9th to 20th Sept) where I was running the system to minimise grid import; on days where the import was <1kWh then the error rose above 2.5% - for example

	GivEnergy Cloud app	Octopus App	error (kWh)	error (%age)
11th Sept	0.17	0.30	0.13	43%
18th Sept	0	0.04	0.04	100%
19th Sept	0.29	0.33	0.04	12.1%

I am not sure that there is too much to draw from this other than there is a small error between the two meters that consists of:

• A small gradient error of <2.5%
• A small offset, noticeable at <1kWh/day
  
• A small amount of noise, noticeable at <1kWh/day

The smart electricity meters that I have had have both been labelled as 'class B' and apparently this means within 1% accuracy, according to IEC62053-21/-22 (but I must admit that I have not read this standard).

 

GivEnergy Cloud App May Export	Octopus Phone App May Export

 

GivEnergy Cloud App September Export	Octopus Phone App September Export

 

For both import and export the smart meter has slightly higher readings than the GivEnergy system, so no particular bias in Octopus's favour or mine (Octopus win a little on import, I win a little on export).

 

My view is that, even with these larger %age errors on low readings, the error band is pretty good and not significant in terms of billing.

 

Consumption Visibility Gives Savings

The GivEnergy phone app gives great visibility as to the instantaneous production and consumption in our home. My view is that, in general, I want to live my life without constraints set by energy consumption, but, having said that, if some easy adjustments mean that we save money then I am all for it.

 

Home Screen: instantaneous production and consumption	Power Graph: 3.3kW peak production - not bad for October	Battery Charge Graph

So how has this helped:

• The first quick win was that we saw that the dishwasher uses a little over 2kWh for each run - so we save around 20p each night or £70/year by remembering to run this overnight at cheap rate (it has a built in timer) - and is usually convenient to have clean dishes each morning (of course, this raises another question on whether to run a half full dishwasher over night! - we usually do).
• Anything with a heater is a high consumer - tumble drier, washing machine, iron, oven...
• Peak rate starts at 4pm and we charge the battery overnight at cheap rate. Export before 4pm gives us around 15p per kWh, export after 4pm around 25p per kWh. So if we get to 4pm with a full battery then we can export most of the contents at 25p/kWh. If we get to 4pm with a less than full battery then we might lose some export at 25p/kWh. So, do the washing on a sunny day, in the morning, then it is likely that you still get to 4pm with a full battery, and we have exchanged laundry for exporting at 15p/kWh, but do the washing on a gloomy day and the battery is not fully charged at 4pm and we have exchanged doing the laundry for exporting at 25p/kWh...

...Keep the laundry for sunny days - perhaps saves of 1 to 4 kWh (10p to 40p), or a £few per year.

• Before going on holiday it is possible to drop the background run rate consumption from 200W to 300W, by around 100W, eg by turning off various low consumption devices like the TV, printer, etc at the wall socket. 100W is 2.4kWh per day, so over a week's holiday that is 16.8kWh or around £2.50.

These are just some specific examples of how the visibility to consumption provided by the GivEnergy app is useful; being conscious of instantaneous consumption can be useful but, in my view, is unhealthy as an all consuming lifestyle choice! Spotting the bigger savings is perhaps worth around £100 per year for us.

 

Mature Technology? Where is this Technology on the Market Adoption Curve?

There is a marketing concept for new technology of the adoption curve

The Technology Market Adoption Curve

The concept of the 'chasm' was introduce by Geoffrey Moore in his book "Crossing the Chasm". My summary of the concept is that many technology innovations look like they have made a promising start, but actually it is the early adopters and visionaries who are buying them. Many technology products fail to convince the early majority to buy in - these people are looking for proof from other users whom they know and trust (ie not fancy marketing). There are plenty of people who disagree with this concept for all technology and for specific technologies. For example, for solar energy, does government incentive (eg no VAT) impact on the adoption curve?

 

My view is that the adoption curve is a useful concept, but is not precise and it is often hard to slot each individual buyer into a neat category.

 

Having said that, it is clear that for a technology product to succeed it must be easy to use and be seen be obviously useful if mass adoption is going to happen

 

My experience, after installation and 6 months with this system, is that solar panels, without a battery, are ready for the majority market and, as long as the numbers stack up, and you have a reputable provider, they are probably a reasonable investment.

 

However, add in a battery and the system is, in my view, very firmly in the innovator/early adopter phase. For a battery to reach its potential return on investment (ROI) it requires:

• Scheduling to get the full return on investment
• Flexible electricity tariffs and this needs a working smart meter (see my posts Smart Meter #1: My Journey and Smart Meter #3: My Journey Part #2).

Both scheduling and getting my smart meter to work has taken more time and effort than, I would have thought, the majority of people would want to spend.

 

My take is that I have selected (perhaps more correctly 'stumbled upon') two of the leading suppliers in the field (Octopus and GivEnergy), but it could be that other suppliers have these issues resolved - it would be interesting to get feedback from others on this point.

 

I do think that both of these issues are in the hands of the industry and, perhaps solutions are close at hand, if the players have a desire to resolve the issues. However, until the issues are resolved, my view is that domestic battery systems should only be purchased where the user is prepared for the effort required as an early adopter.

 

Summary:

The system is operating reliably and the software is good, with the exception of the scheduling points above (& more detail in a future post) and the issues with the smart meter (now resolved).

 

I am also coming to the conclusion  that the domestic solar+battery energy industry operates as a 'cottage industry'. It works well for enthusiasts, prepared to invest time and effort and is not really ready for mainstream users...

 

...but it is probably the case that the domestic solar (with no battery) makes life much easier, is a more mature market and is ready for the mainstream.

Smart Meter #4: So, How do you Fix Smart Meters?

Introduction

Am I just lucky? Did my smart meter spring into life due to some alignment with the technology gods on that Friday afternoon in September in a garage in Blackburn? Are there wider lessons to be learnt? 

 

 

Raising the Height of the Communications

Hub was the Solution to Poor WAN Signal

for me in Sept 2024 - the 6th engineer visit from Installation in June 2021

 

0.7% or 10%?

Some Googling gave me two numbers:

• The Data Communications Company (DCC) claim that only 0.7%  of premises (or around 200,000) are not covered by their Wide Area Network (WAN).
• UK Government/BBC say that over 10% or 4 million smart meters do not work (see my post "4 Million Smart Meters Don't Work") because they cannot communicate with the energy providers (via DCC).

 

However, the BBC report is about both gas meters and electricity meters, my focus is really the electricity meter. For these the  Department of Energy Security and Net Zero (Desnez) figures for June 2024 are 1.4million smart electricity meters not working out of 19.5million installed, or 7.1%.

 

But the difference is still over 10x; why?

 

The plot thickened when I read that Ofgem (the industry regulator) says that the DCC is obligated to provide coverage to at least 99.25% of premises in the UK and the DCC subcontractor for the Northern region, Arqiva, claims, in their June 2022 Annual report to have had 99.5% coverage in 2021, for the North of England and Scotland. 

 

What does coverage mean to DCC and Arqiva. I could not find their definition nor any details on where the 200,000 premises that are not covered are located. Does coverage mean something different to 'a signal good enough to make a smart meter work'? Is it that the signal is fine and the installation engineers are just no good at installing the meters?

 

...and then another question occurred to me: the over 10%/1.4 million non-working smart electricity meters reported by Desnez seems to cover only smart meters not operating in smart mode (ie "traditional mode"). Are there other meters like mine that for a period (before raisingthe height of the comms hub) missed readings on some days? How many people has this affected?  Does this issue matter? Will the users of these smart meters be able to use flexible tariffs and be rewarded for their contribution to getting to net zero?

 

Why the 10x difference?

Can both 99.5% coverage of premises in the North and 10% non-smart smart meters both be correct? I found this diagram on the DCC website:

The Smart Meter Eco System is Complicated - this diagram is from https://www.smartdcc.co.uk/our-smart-network/

 

To me, it sounds like there are different view points from within this eco system:

• Arqiva and DCC are in the 'it's all on plan' camp - all we need to do is get the last 0.7% working
• The Energy suppliers provided the figures for Desnez and they seem to be in the 'Houston, we have a problem' camp.

 

The responsibilities are perhaps a clue:

• DCC are responsible for the WAN and providing the Communications Hubs that connect smart meters to the WAN (the DCC budget report gives a figure of £138 million to be spent on communications hubs in 2025, rising to over £200million in 2027)
• The energy suppliers are at the 'coal face' having to install individual smart meters and get the comms hub to work.

 

Now, if one individual engineer is struggling to install working smart meters then I might believe that the problem is with that engineer. But if many engineers are struggling, then the issue must be in the bigger picture. My discussions with engineers visiting my home gave me an impression of a highly competent and knowledgeable team, keen to help and get my smart meter to work; however, the equipment and options available to them are limited to replacing a meter and requesting someone at their base to see if they can connect to it over the WAN.  My take is that while in some cases there may just be a need for a stronger signal, it could well be that giving installation engineers more backup and options may resolve many issues:

 

• Comms hub - sensitive for weak signals
• Comms hub - signal transmission strength
• Help with identifying obstructions to the signal
• Comms hub mounting options to avoid obstructions to the signal
  
• An instrument to measure signal strength
• Better information provided to engineers
• A proven and standardised Installation process
• Etc.

 

My take is that installation engineers understand the issues, for example, one engineer who visited me, feels that obstruction to the signal is an issue affecting many homes that he has worked on, eg a house with a non-working smart meter in a cellar Vs next door with a working smart meter above ground. Is the reason for the gap (0.7% Vs 10%) that, while many homes have a good signal outside, the smart meter comms hub is not designed to suit the location of the meter in these houses? Perhaps for 9.3% of homes, while there is good signal in the street outside, the electricity meter is in a location in the house with poor signal?

 

It would be interesting to get DCCs explanation for the gap between the 0.7% and 10% non-working meters. What is wrong with the 9.3% of meters/comms hubs that are in areas covered by DCC and the smart meter does not work?

 

Does Missing Data / Unreliable Coverage Matter?

 

In my case, I had a period of a few months where data for odd days was not reported - I have not seen a number for how many meters are affected by this issue of unreliable data reporting. My sense is that this number is in addition to the 10% of meters working in "traditional mode". Is this a significant number? Is this a significant barrier to receiving the financial incentives for low carbon technology (heat pumps, EVs, solar panels/batteries)...

 

...on the latter question the result for the customer (in my case) was a phone call to Octopus every month so that they could manually request the missing readings. Now, while the people at Octopus are very nice, I would not want this as a long term relationship - I lasted just over 2 months before I got frustrated with the situation. Perhaps I lack patience. I would be intrigued to know what Octopus do if the customer does not request the missing readings? Do they bill on standard (non-flexible) rates or would they, unprompted, make the request for missing readings. My experience is that Octopus bill at standard rates, rather than requesting a manual re-read of missing data, unless they are prompted. Of course it could be that I was unlucky and this is not typical.

 

Energy suppliers and smart meter users with unreliable connections please do comment.

 

If my experience is typical then users with unreliable WAN connections will be on standard rates and miss the return on investment possible with flexible rates. How many premises have unreliable data reporting from their smart meters?

 

Smart Meter Reputation

Martin Lewis points out that the problem is worse than the 10% of non-working meters  - the 'brand' reputation of smart meters is so poor that the 40% of people who do not have a smart meter yet will not want the hassle of getting one.

 

How do we fix this?

During my career as an Engineer I have run into a few cases where systems did not perform as expected by customers. Each situation is different, but there are a couple of common threads in resolving problems:

 

• Transparency - data available to all stakeholders (in this case: users, DCC, Energy suppliers, journalists, politicians...)
• Clarity on who is responsible for each part of the problem and solution.

 

Transparency

Imagine a map of the UK showing the latest smart meter data, published on the internet, available for anyone to read - view the whole country or zoom in to street level.

 

It would help engineers install meters if the map showed signal strength and areas not covered by the DCC WAN. It would help show if this is a straight forward install in an area with good signal or do they need to worry about optimising the comms hub location?

 

Showing WAN radio mast locations might help engineers assess obstructions to the signal; potentially useful when refining hub location.

 

Where smart meter users give permission, then the exact location of their meter could be shown and, perhaps by postcode area for those who wish to be anonymous. With each meter its WAN status and if problems who is responsible for fixing this (eg Octopus, Ovo, DCC...). If I see that all of my neighbours have working smart meters, then perhaps I will say yes to having one installed?

 

Zooming out on the map might give statistics for an area: %age of working smart meters, %age with issues, etc. Easier for everyone in the DCC team to see where they have problems still to solve.

 

As well as practical help for engineers this would also highlight accountability for solution (eg energy provider or network provider)

 

Clarity on Who is Responsible

Ofgem also have a handy guide: https://www.ofgem.gov.uk/energy-policy-and-regulation/policy-and-regulatory-programmes/smart-meter-transition-and-data-communications-company-dcc

The smart meter eco system is complicated. Below is my take on who is responsible for some of the key parts of the problem. These organisations all employ talented engineers with in depth knowledge of the problems, but I couldn't resist adding my thoughts (based on limited information)  on what might be  done to help solve the problems:

What responsible Possible solutions (just my thoughts) Installing Smart Meters Energy Suppliers Clear, proven process on what to try if a smart meter WAN does not work. Providing Comms hubs DCC More options available for 'weak signals', eg longer flying leads and weather proof boxes to mount comms hubs high up (like mounting a TV aerial or satellite dish) Published instructions for getting a Comms hub to work DCC? It might help if everyone did the same thing and then the process could be refined as solutions are found. Published mapping of radio masts, signal strength as well as working / non-working smart meters. DCC? Is the problem weak/no signal in the area or an issue with a specific obstruction, between the meter and the radio mast, in a good signal area? This would help inform the solution - a new location for the comms hub or just accept, for now, that the house is one of the 0.7% not yet addressed by DCC. Signal strength meter for each installation engineer DCC? This should help avoid 'trial and error engineering' for comms hub location and should speed up installation if the engineer could measure the signal strength before she/he starts work and, in the event of problems, provide better data to DCC engineers. Collecting data from smart meters DCC If just some data is missing, on an otherwise working smart meter, automatically retry reading the missing data. Flag an issue for a DCC engineer to resolve if the problem persists. Collecting data from DCC Energy Supplier If just some data is missing, on an otherwise working smart meter, automatically retry reading the missing data from DCC. Flag an issue for the energy supplier engineer to resolve if the problem persists.

 

Energy suppliers and DCC - please do comment on your areas of responsibility - have I got this right?

 

Smart Meter Reputation

Word of mouth is key. If a customer ends up waiting in all day on 5 occasions for 5 engineer visits then they will likely say bad things about smart meters...

...on the other hand, if installation is smooth and quick and users see benefits of £100's per year then the remaining 40% will be clamouring to have a smart  meter installed.

 

Additional Costs

A web based reporting tool, comms hubs with longer remote cables, instruments for measuring signal strength, etc will have a cost...

...but, based on my experience, getting a smart meter to  work, with the current (trial and error) approach in an area with less than perfect signal (but still within the 99.7% DCC WAN signal coverage area?) means:

• 6 engineer visits
• 3 comms hubs tried
  
• 2 electric meters tried
  
• 2 gas meters tried
  
• 28 phone calls with the energy supplier
• 43 e-mails with the energy supplier

It sounds like this cost will be multiplied by 1.4million non-working smart meters amongst the current install base.

 

I am guessing that the initial investment in a more intelligent approach to installation would be quickly repaid. I am also guessing that, without a robust and reliable product, the costs will only increase in trying to convince the remaining 40% of the population to convert to smart meters and/or trying to force an installation on an increasingly reluctant public...

 

...and then there is the cost to the planet if we do not accelerate  the adoption of new technology like heat pumps, EVs and solar panels - if these are shown to save money then the adoption will accelerate - robust smart meter communication is part of that.