Regular readers will know that my Energy Smart home includes a storage battery. That battery is either charged from my solar panels (effectively free electricity), or low cost electricity bought from the grid, or some combination of the two depending on the solar forecast for the day ahead.
The logic of how much battery charging is required has until now been driven by a set value for the number of charging hours required. The number of hours of solar charging predicted is deducted from the the number of charging hours required to calculate the number of bought charging hours required outside the solar production window.
Bought charging hours required :=
Total hours required – Solar hours predicted
However with experience this appears to be a sub-optimal arrangement. At one extreme on a very sunny day the battery will fully charge and then be allowed to discharge continuously through all other hours, there is no middle ground in which the battery is not permitted to discharge through the night. However at the other extreme if the battery is replenished entirely from the grid then there will be hours when discharge is not permitted since, after accounting for cycle efficiency, the value of the electricity in the battery is higher than the cost of that from the grid and thus it’s better value to use grid electricity than stored electricity. As there are fewer discharging hours then fewer hours of charging will be required to refill. Thus the depth of discharge is greater when charged from solar than from the grid requiring more charging hours to refill. Leaving the longer charge time for a full charge in use then creates a risk of charging the battery when the grid price is higher than necessary leaving the battery possibly full by the time the lowest cost grid energy is available. Having a more accurate target for the charge time would enable the lowest cost charging periods to be selected more precisely.
The new refinement is to automatically adjust the bought charging hours between two existing user-defined values: the existing target hours and the maximum charging hours currently used just to cap charging hours during plunge pricing events (i.e those with negative cost events). The new algorithm can adjust to any value between the two limits in half hour intervals. As currently configured that’s anything between five and seven hours. The new algorithm is:
Total hours required := minimum (Maximum hours from plunge, maximum (Target hours, Solar hours predicted + 1))
Most of us are used to a simple world of electricity where we pay for what we consume. For most folks like myself based in the UK that’s typically a fixed price per kWh/unit consumed regardless of time of day, even through dual-rate tariffs have been around for decades – the best known being “Economy 7” tariffs. However as the grid gets smarter then there are increasing opportunities to save on, or make money from, electricity.
Conventional – pay for power.
This is the area with which most of us are most familiar. We all get the idea of paying for the power we consume. Most UK households pay a fixed price per kWh/unit regardless of the time of day. We have a competitive electricity market, so there are the choice of 70 to 80 different providers who will make different offers regarding standing charge (sometimes marketed as a subscription) and unit cost.
There’s also the opportunity to choose between a flat rate tariff or Economy 7 even on conventional meters that provide a discounted night rate for 7 hours.. These typically provide a discounted night rate, but may charge a little more during the day. They used to advertise these as ‘less than half-price electricity’ but that’s often not the case now.
Stepping up in complexity (and opportunity) smart meters provide the opportunity for a more diverse range of tariffs including different cheap night time periods, more than two rates at different times of day (in extreme 48 half-hourly rates), and a free day at the weekend (i.e. a zero rate of a weekend day) etc.
Beyond that my own tariff (Octopus Agile) not only has up to 48 different half-hourly prices/day that change daily based on that day’s market prices. That might sounds a bit scary but it can yield very cheap electricity prices – 4.48 p/kWh for me in April/May 2020 (for example) which is a third of what most people pay.
(The original version of this post wrongly had the table from my gas bill above and mistakenly claimed that I had paid “a quarter of what most people pay” rather than a third. Total consumption is untypically low at the present time due to limited miles driven.)
Agile – paid to consume
Top left on my initial diagram is Agile – paid to consume.
One of the features of the wholesale electricity market is that at times the market price for electricity goes negative. At such times the a significant excess of supply (typically because of high output from wind turbines) over demand (often but not always at night) yields a negative price so electricity companies looking to buy electricity are being paid to take it. Most electricity companies will continue to charge their customers the standard price in these circumstances but, with the octopus Agile tariff, the negative pricing is passed to the consumer so that you are paid to consume electricity. This is one of the reasons that my electricity costs are so low.
The above chart shows my electricity costs for Saturday 23rd May 2020. The blue line shows the half-hourly electricity price varying between minus 10 p/kWh and plus 15 p/kWh. The red bars show my electricity consumption in each half hour. You can see how consumption tends to be highest when the price is lowest leading to an average price paid of minus 6.22 p.kWh (i.e. they paid me to use electricity) – indeed they paid me 82.4 p to buy electricity that day.
Conventional export – paid to export
The next opportunity to make money from electricity is to sell it to the grid. Obviously that depends on having a source for the electricity typically a generating asset like solar panels or a wind turbine, possibly coupled with a storage device like a battery. It’s also possible with a battery alone, but I know no-one who does that as the economics are more challenging.
The UK currently has a scheme called Smart Export Guarantee (SEG) where you can sell your export to an electricity company. Prices vary enormously so it’s worth shopping around and not just assuming that your electricity company will give you a good offer.
There is also a smarter SEG option where Octopus offer a dynamic SEG based on market rates (Octopus Agile Export) which may at times offer a high rate, but also offers a lower rate at times, and is thus perhaps better suited to those with storage.
I myself am NOT on such a tariff as I’m on an older legacy Feed-in Tariff (FiT). Despite its name FiT is a generation incentive, not an export incentive. As a generation incentive FiT encourages self-consumption since each kWh that I consume myself does not reduce my income, whereas on SEG each kWh that I use myself (such as making hot water) would reduce export income. So, for example, if I use a kWh of electricity to make hot water that’s saved a kWh (or thereabouts) of gas at around 3 p/kWh, but if I was on SEG then I might have lost 5.5 p/kWh of export revenue to save 3 p/kWh on gas which is clearly an on-cost not a saving. There are other benefits of course because I’ve reduced my carbon footprint by using my own low CO2 electricity to replace a fossil fuel, but it’s not (in this case) improving my financial position.
A further area of research by others is V2X (V2H and V2G) – taking electricity stored in an electric vehicle and using that within the home (V2H) or exporting it to the grid (V2G).
Export penalty – penalised for export
A logical consequence of this smart grid that I’ve outlined is being penalised for export. If there are times when the market price for electricity is negative then if I were part of that market then I might expect to be penalised for export. This doesn’t actually exist in the UK, as the only model that links SEG payments to the market price, Octopus Agile Export, protects its customers from negative pricing.
Should consumers be exposed to this risk then a logical behaviours would be:
To manage self-consumption into the negative export periods, and potentially thus increase export in the positive export periods. For example disable diversion to an immersion heater or car when export price is positive, and then maximise self-consumption when the export price (and presumably the import price also) is negative.
To disable the generating asset to avoid the export penalty.
Some people like myself will find developments in the smart energy sector a fascinating and engaging topic with opportunities both the save money and engage in creating a cleaner and greener electricity system.
However given that many choose not to even participate in the competitive market for electricity supply created when the regional electricity companies were privatised in late 1990 (i.e. 30 years ago) then there will be a significant number who are not so motivated.
This then creates opportunity for a wider variety of smart offers. Some products, at the Agile Octopus end of the spectrum, giving the consumer the opportunity to benefit from their own decision making, while others look more like a traditional dumb tariff with a very simple price structure but potentially making the energy company a more active manager of the home appliances so that the consumer hopefully plays a lower unit rate while the energy company takes responsibility for managing the assets within the home.
CO2 production is increasingly of interest as the world struggles to limit man-made climate change. As we use different energy sources each represents a certainly amount of CO2 reflecting a combination of the energy invested to create that power source (e.g. the wind turbine may generate wholly renewable power, but its construction created some CO2) and the CO2 created as it generates energy once constructed (nothing for renewables but relatively high for fossil-fuelled generation).
I’ve previously shared this table showing the IPCC’s view of the embedded CO2 in different sources of electricity generation.
A recent question and resulting discussion in an on-line forum prompted me to think more about the area of embedded CO2.
My first observation would be that my rooftop solar panels do quite well on this scale with a CO2 figure of 41 gCO2/kWh.
The second observation would be regarding energy storage. My view would be that any energy storage device from a small scale domestic battery like my own to a large pump storage scheme can never deliver better embedded CO2 that the source of its energy. So, for example, if I charge my battery from my own solar at 41 gCO2/kWh with a cycle efficiency of 80% (the maker’s claim) then the embedded CO2 in the energy coming out of the battery cannot be better than 41 gCO2/kWh / 80% = 51 gCO2/kWh. Indeed it would be worse than that as this doesn’t account for the CO2 generated in creating the battery nor its operational life, but I don’t have figures for those.
Thirdly, as my own embedded CO2 is relatively low whether exported directly from my panels or indirectly via the storage battery, then the CO2 intensity of the grid always benefits from my export. The 116 gCO2/kWh illustrated above is pretty low for the UK grid which varies widely but is still more than my solar PV directly or stored solar PV. Indeed had I exported onto the grid at the time illustrated above then my 41 gCO2/kWh versus the grid’s 116 gCO2/kWh would have saved 75 gCO2 for each kWh that I exported.
However if, for example, I export electricity but need to then buy more gas to make hot water then that too has a CO2 impact.
If I need to buy a kWh of gas to make hot water that’s 0.2 kgCO2/kWh or 200 gCO2/kWh even before I’ve accounted for the relative inefficiency of the gas boiler versus my electric immersion heater. If I assume that the gas boiler is 90% efficient then I will be responsible for 200 gCO2/kWh / 90% = 222 gCO2/kWh for a kWh used to make hot water. Thus, while exporting 1 kWh of solar PV may save the electricity grid 75 gCO2/kWh, it’s added 222 gCO2/kWh to gas consumption – a net deterioration of 147 gCO2/kWh.
Natural gas of course is the lowest CO2 of the fossil fuels listed above – if your home is heated by oil, coal or wood then the analysis is further skewed towards using your own self-generated power rather than exporting electricity and importing another fuel for heating.
The electricity grid’s carbon intensity also varies. In 2019 the UK average was 256 gCO2/kWh (a little higher than my estimate for gas) however this varies considerably through the year with the highest embedded CO2 in early winter evenings when I have little if any solar PV to contribute to the grid, and may well be lowest when I and others have surplus solar PV. My understanding is that the lowest grid CO2 occurs with a combination of high renewables (such as particularly windy weather) coupled with low demand (such as summer nights).
Thus my own strategy is to:
Maximise self-consumption of my own solar PV as my energy source with the lowest embedded CO2 (except in the event of an extreme plunge pricing event when the grid is under highest stress)
Make best use of storage to minimise consumption from the grid in the evening peaks when embedded CO2 is likely to be highest.
When a solar-shortfall is anticipated then buy electricity selectively from the grid at lowest CO2 (using Agile electricity price as a surrogate for CO2).
For some time now I’ve been thinking about creating a real time display which pulls together data from a variety of sources around the home to provide an overview of what’s going on without the need to visit multiple web pages or apps. Until the last 10 days or so that involved little more than thoughts of how I might evolve the existing immersun web page with more content (I don’t have the skills to write my own app), but then about 10 days ago I saw an online gauge that someone else had created to show energy price and inspiration struck. Ten days later I have my monitor working, albeit not complete:
The monitor pulls together information from:
My electricity tariff for p/kWh
My immersun for power data (to/from: grid, solar, water, house)
My storage battery for power in/out and state of charge
My HEMS for electricity cost thresholds between different battery modes.
The gauge consists of two parts: (i) an upper electricity cost part and (ii) a lower power part.
The upper electricity cost part is effectively a big price gauge from 0 p/kWh to 25 p/kWh with a needle that moves each half hour as the price changes. It has various features:
The outer semi-circular ring (blue here) shows today’s relationship between battery mode and electricity price. Today is very sunny, so no electricity was bought from the grid to charge the battery, and this part is all blue for normal battery operation. If the days was duller and electricity was to be bought to charge the battery, then two further sectors would appear:
a dark green sector from zero upwards showing the grid prices at which the battery would be force charged from the grid, and
a light green sector showing when the battery is not permitted to discharge but may continue to charge from solar.
In inner semi-circular ring (green / yellow / red here) currently just colour-codes increasing electricity price, but will be used to show today’s prices at which car charging and water heating are triggered from the grid.
The current price per kWh is taken from Octopus’s price API, while the current cost per hour is derived both from this and the grid power from the immersun.
The needle grows from a simple dot indicating the price per kWh only when no power is drawn from the grid to a full needle when the electricity cost is 10 pence per hour or more.
The lower power part is effectively a power meter ranging from 5,000 Watts of export to the left to 5,000 Watts of import to the right. It updates every few seconds. It has various features:
The outer semi-circular ring (orange /maroon / green here) shows how power is being consumed:
orange – shows consumption by the house less specified loads
maroon – shows battery charging
blue (not shown) – shows water heating
green – shows export to the grid
The inner semi-circular ring (yellow here) shows the source of power. The sum of the sources should equal the sum of the consumers. The sources are:
maroon (not shown) – shows battery discharge
yellow – shows solar power
red (not shown) – shows grid power
The power value shows the net import or export from / to the grid, while SoC refers to the state of charge of the battery (0-100%). The combination of import power and electricity price gives the cost per hour in the top gauge.
The needle position shows net import (to the right) or next export (to the left). The needle should thus be to the left of the green sector, or to the right of the (unseen) red sector. Needle length show the full power being handled and is thus proportionate to the angle of the sector including all the colours in the lower gauge and extends from 0 to 5 kW.
The gauge scales to fill the smallest of screen height or width and translates to be centrally positioned regardless of screen size. My intention is to display it on an old mobile phone as an energy monitor, but I can also access it on any web browser on any device within the home.
As winter turned to spring my thoughts for my HEMS turned to thinking about how to adjust the operation of the HEMS in managing battery charging to account for anticipated solar production. Previously the HEMS was configured to buy a preset number of hours of charge from the grid each day, typically overnight when the power tends to be cheapest. Through 2019 as the seasons changed I periodically adjusted this figure to create headroom to store charge from the solar panels later in the day. However I would like to make this adjustment automatically day by day.
Late last year I came across solcast a website that predicts the output of solar panels. The user creates an account, describes their PV array (location, capacity and orientation), and can then download predictions via API.
The orange line shows the predicted output for the next few days, with the light grey area showing the confidence interval from 10 to 90%. As a prediction there’s a degree of uncertainty associated with the prediction as there is with a weather forecast. The 10% line shows that 1 day in 10 the output will be lower than the grey area, while the 90% line shows that 1 day in 10 then output will be higher than the grey area.
My original prediction were based on the orange line (the 50th percentile) where output was equally likely to be above or below this amount. However my risk on an incorrect prediction is not even. If I fail to buy enough power from the grid when the price is low then I risk paying up to 35p/kWh to buy when the price is high, whereas if I buy an unnecessary cheap kWh from the grid I may spend an unnecessary 5p/kWh on average. Thus I decided to take a more conservative position on risk to obtain the lowest cost position. I opted for a 20% risk, so 1 day in 5 I might underestimate my purchase from the grid reflecting the ratio of grid prices high:low. I estimated this 20th percentile assuming a normal distribution on the low side where the 20th percentile = 0.34 * 50th percentile + 0.66 * 10th percentile.
The process is as follows:
download the data in a half-hourly format to be comparable to the half-hourly Agile electricity price data,
calculate the 20th percentile from the 10th and 50th percentiles,
count the number of half-hours in the 20th percentile data above a threshold that provides for charging the battery at full power,
establish the earliest half hour when solar would charge the battery at full power,
adjust the period for buying power from the grid by the period anticipated for solar charging (tomorrow a total of 7 hours is to be achieved by 6.5 hours from solar leaving 0.5 hours from the grid),
adjust the end time for buying power from the grid to align with the earliest hour when the battery can charge at full power from solar.
The result of these calculations can be seen above. One half-hour of lowest price battery charging has been identified overnight to meet the requirement for charging from the grid. Normal HEMS behaviour has also identified several other periods of grid charging at lower cost during the day, but these are not counted towards the target for buying from the grid due to the potential for a double count of solar and grid charging during the day. (In a similar manner there are multiple start times during the afternoon when the dishwasher or washing machine can be run on grid power more cheaply than the optimal overnight start time.)
No explicit command from the HEMS is required to enable proportional charging of the battery from the solar panels when there is a solar surplus as 2 of the 3 used battery operating modes (normal and charge-only) have this capability, and the third mode force charges the battery. If the battery is force charged during solar surplus then the source of the energy will of course be the solar panels, but any shortfall will be met from the grid.
Proportional to solar surplus
Proportional to shortfall
Used at high grid prices (today discharging enabled at > 8 p/kWh)
Proportional to solar surplus
Used at mid grid price to save stored energy for period of higher grid price (between 5 and 8 p/kWh today)
Used at lower grid price to buy from grid (today buying at < 5p/kWh). If low price occurs at time with reasonable solar generation then use of solar output will happen automatically, with only any shortfall coming from the grid.
Solar PV installations like mine that are a few years old generally qualify for the UK’s Feed-in Tariff (FiT) which pays both for generation and notionally for export, while newer installations are covered by the Smart Export Guarantee (SEG). The older FiT scheme was universal in the sense that all larger electricity companies had to participate and they all paid the same rates, while with the newer scheme there’s still an obligation for larger companies to participate but the rates are all different. Older installations like mine can optionally swap the export component of the FiT for the SEG, but is that an attractive option?
SEG payments differ widely between providers so it’s worth shopping around.
My FiT export payment is currently 5.38 p/kWh on a deemed export basis, which means that, rather than measure actual export, it is assumed that half of my generation is exported. My electricity supplier Octopus offerers one of the best SEG rates at 5.5 p/kWh but that’s on the actual export, not the deemed export.
2,098 kWh (50% of 4,196.1 kWh)
Switch to SEG without other changes
Add disable water heating from solar to above.
1,722.4 kWh (1,075.3 + 647.1 kWh)
Provide equivalent water heating from gas
3.2 p/kWh / 90%
Octopus Energy does also offer the alternative of a variable export rate based on wholesale prices, akin to their Octopus Agile import tariff, but for export. However it’s my belief that I would need a much larger battery than I have now (4 kWh) in order to benefit from this as it will always be generally better value to use that stored energy to avoid the early evening peak price period (up to 35 p/kWh) than to sell it back to the grid at a lower price and then need to buy more energy myself. If I had a bigger battery (both in terms of energy and power) then I could both meet my own needs and sell back to the grid.
Overall however I think that it’s clear that, with my current relatively small battery and deemed export tariff, I’m better off on the older FiT scheme than the newer SEG scheme even with one of the better-paying SEG providers.
I’ve been seeing a few online advertisements recently touting 70% savings on electricity through a combination of solar panels and battery storage. I’ve also been looking for a way to express my savings through my smart tariff so this seemed like a opportunity to try that.
My start point is a years data from my monitoring system..
I also went through a year of electricity bills (with slightly different start and end dates) concluding that my average purchased electricity cost was 7.08 p/kWh. Thus my average electricity costs (including solar) are on the right of the table below:
est unit price
my uniT price
my saving v. Est
Total / Average
Comparison between my electricity cost and the UK average
If I look at the Energy Saving Trust’s assumptions as a baseline, they have the average UK cost of electricity as 15.75 p/kWh. If I’m paying an average 4.5 p/kWh for each kWh used with my combination of solar PV, storage battery and smart tariff then I’m paying 28.6% of the cost of someone who used the same amount of electricity bought at the average UK rate or saving 71.4% of electricity cost. To put it another way, I’m paying £305.23 for electricity that would have cost the average UK consumer £1,068.32 (on the left of the table above) – a saving of £763.09.
(The baseline assumption that someone would have used the same amount of electricity as me without my level of technology is a slight over-estimate as I flex water heating between gas and electricity since my bought electricity price is sometimes lower than my bought gas price causing me to substitute electricity for gas. Someone on a conventional electricity tariff and gas would never make that substitution as their gas would always be cheaper than their electricity, hence my electricity consumption is a little higher than someone who would be on a conventional electricity tariff.)
I’m also generating feed-in tariff due to the age of my system (approximately 4.5 years old) which would be £714.59 per annum at current rates, and making 1,075 kWh of hot water from surplus solar electricity which saves £38.22 in gas (the diverted / hot water saving in the screenshot above is based on a notional electricity price, not a gas price). Unless I’ve missed something that’s an annual return of £1,515.90 (£763.09 + £714.59 + £38.22).
In my previous post I estimated my investment at £8,670 so with an combined annual savings and revenue of £1,515.90 that’s a 17.5% return or a payback of 5.7 years. Previously I’d estimated 9 years including the battery, but this was without the benefit of the smart tariff. As we’ve now had the solar PV for 4.5 years that’s very promising, although as my return seems to be accelerating it will take more than 4.5 past years + 1.2 future years (total 5.7 years) to achieve payback.
The current 5.7 years to payback would have achieved payback in spring 2021 as the near bookend, while the prior 9 years would have been autumn 2024 as the far bookend. In practice I could not have achieved the lower bookend of 5.7 years, even had I invested in all the supporting technologies simultaneously, because I’m combining the legacy Feed-in Tariff (FiT) scheme for my solar PV with the Octopus Agile dynamic smart electricity tariff which started in February 2018,
Discussion elsewhere prompted me to look into what I spent on what you might term my energy smart systems relating to electricity consumption, so I thought I’d document it here.
Solar photovoltaic system (4kW)
Bundled with ImmerSUN.
Powervault battery storage (4kWh)
Free installation as part of UKPN trial.
ImmerSUN management system with monitoring.
Estimate based on today’s pricing.
Remote-controlled car charger.
Modified used charger from eBay. My own software.
Raspberry Pi items to make HEMS
My own software.
Wet goods automation (WIFIPLUG x 2)
Prior analysis of items #1-#4 in pre-Agile days has suggested a total of 9 years to achieve payback on this investment through use of around 85% of the generated energy. Solar panels are potentially good for over 20 years operation, although I doubt the lead-acid batteries will still be operating for anything like that long.
The combination of item #5 with my Octopus Agile dynamic smart electricity tariff has resulted in my average bought electricity price being 7.75 p/kWh in 2019, about half the UK average. I suppose that I could make the same judgements and program items manually each day, but the HEMS significantly reduces my time commitment to achieve that.
Item #6 is my most recent addition. The sophistication of the algorithm combining the Agile tariff with a simple model of the cycle of each device is such that I would never achieve such a high quality result manually. However the saving is perhaps only a three pence each day so maybe £10 per year on my Agile tariff and thus 7 years to pay for the two smart plugs.
Much of this content is thus around 7 years to payback. The HEMS is potentially much quicker, but relies on having smart systems to control such as battery storage and car charger.
One of the consequences of integrating a smart home is the large number of different apps, web portals and potentially sources of APIs involved. The ones I use include:
Reads and stores consumption from smart meter.
No price data for my tariff due to smart meter limitations.
Eve’s alternative to Home for all HomeKit accessories with additional functionality for Eve’s own devices.
I prefer this to Home for editing rules. I use Eve products mostly for central heating control.
Apple’s own app for the HomeKit smart home ecosystem.
Need to refer to device manufacturers own apps (such as Eve or WIFIPLUG) for some configuration and data.
My own web portal to view HEMS schedule and status via Apache web-server on Raspberry Pi.
Control of ImmerSUN power diverter.
Available API provides some measurement and status data as per main screen of the app.
Control of Powervault storage system.
Available APIs provide some user scheduling and status capability.
Future cost, and historic costs and consumption (30 prior days) from Octopus (electricity supplier).
APIs provided by Octopus. App developed by an enthusiast using Octopus APIs. Octopus’s own web portal provides historic consumption but does not pair this with cost. Monthly statements show graph of consumption and cost for each day.
Control and measurements from own brand smart plugs.
Plugs also appear in Home and Eve apps. I use for dishwasher and washing machine.
Notes to table:
APIs not officially released. Reverse-engineered by an enthusiast and available on line.
APIs not officially released. Used as part of a sponsored trial when I first got the battery and re-used by myself with some manufacturer support.
iOS only. Not available for Android.
Some of these apps have similarities:
Both Bright and OctoWatchdog show whole of house energy consumption (and potentially cost) derived from the smart meter. However they have differences too. A smart meter sits on two networks: (i) the Wide Area Network (WAN) via which the meter communicates with the energy supplier and (ii) the Home Area Network (HAN) which links the devices in the home (electricity meter, gas meter, CADs/IHD and gateway). Bright connects to the HAN via small piece of hardware called a Glow Stick Wi-Fi CAD and collects its own data in real time and stores its own records of energy consumption in the cloud; while OctoWatchdog involves no extra in-home hardware, and takes data a day in arrears from Octopus not storing anything in the cloud itself. Bright’s USP is the real time consumption and current day’s data (neither of which OctoWatchdog supports), while OctoWatchdog’s USP is the availability of electricity price which isn’t available from the meter.
Both Eve and Home interact with all devices in the whole HomeKit ecosystem. Eve is best for creating rules and has more ability to configure Eve’s own devices, while Home is best for sharing access with family members. WIFIPLUG’s app is more limited only interacting with their own devices, and thus cannot see Eve or other HomeKit devices.
Both MyImmersun and WIFIPLUG apps, and the Powervault portal, allow configuration of their own manufacturer devices. They all have, for example, timer capability and data logging. MyImmersun is better for giving a whole-of-home view showing solar panel output and net input to house (so provides a more comprehensive energy monitor), Powervault shows no solar panel output but does give a view of whole-of-home, while WIFIPLUG provides only a view of the energy consumption of devices plugged in to the WIFIPLUGs.
Yesterday provided a good example of my HEMS in action as the electricity price dropped quite low due to stormy weather conditions. Normally at this time of year the HEMS isn’t doing much with the storage battery as daytime solar output is enough to fully charge the battery, but yesterday low pricing was enough to automatically enable both battery charging and water heating overnight. Car charging was due to run anyway driven by the demand for an hour of charging, but battery charging and water heating was triggered by the low price rather than a needed to take power for a pre-defined period of time.
The screenshot above from my phone shows the HEMS’ plan for the the early hours of the 9th. The first price column shows one hour of car charging at the cheapest price. The second column shows half an hour of water heating as the electricity price has fallen below 3.5 p/kWh when it is assumed to be cheaper than gas. The third column shows four hours of battery charging when the electricity price is below 5 p/kWh.
The above image from the HAN side of my smart meter shows the energy consumption of the house varying through the night in response to these requests from the HEMS – battery charging at the widest point, car charging above that for an hour, and water heating above that for 30 minutes.
Finally this image shows the energy consumption versus price data for the same period shows how the action of the HEMS increases electricity demand as the price drops. Indeed on this day there was virtually no consumption at any other time.
For August 9th as a whole I paid 52 pence for 7.547 kWh of electricity. Taking off the 21 pence for the standing charge leaves 31 pence for the electricity kWhs alone, an average of 4.11 p/kWh.