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.
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.
It’s now around a year since I first started with my HEMS. Initially it was managing just the charging of my electric car around the cheapest electricity prices, but subsequently I added water heating, storage battery control and, most recently, dish washer and washing machine.
That original HEMS was built around a used Raspberry Pi that I bought cheaply from a colleague but, while its processing power was perfectly adequate for the task (which isn’t at all demanding), it did have some mechanical issues. Firstly, the mounting holes for accessory cards (known as HATs – HArdware on Top) moved between generations of Raspberry Pi and the holes in my Pi for the standoffs did not align with those in the newer accessory card. Secondly, I never managed to find a case with the space for the HAT and the mains cables that it switches. The former resulted in the electrical connector between the Pi and the HAT taking much of the mechanical load, and an occasional loss of connectivity to the HAT.
These mechanical issues are resolved by the new HEMS which uses a third generation Raspberry Pi allowing use of proper standoffs and a new case with the depth to enclose the HAT too.
As previously two cables connect to the HEMS – a black USB cable bringing 5V power and a grey multi core cable that brings a live mains feed to the relays with three switched lives returning to the adjacent junction box. The relays control my less intelligent loads – the ImmerSUN and the car charger – while other more intelligent loads are controlled via APIs via Wi-Fi and the cloud. The whole assembly continues to be mounted on a board on the side wall of the airing cupboard. When modification is required the assembly can be lifted off two screw heads and laid on the floor.
All the software on both generations of HEMS is the same except for the scripts that interact with the relays – either to set or read status. I assume that the pinouts on the Pi must have changed between generations as an identical HAT card moved from the ic0 to the ic1 bus requiring a single digit change to each interacting command.
So, over the last few days I’ve been adding control of my wet goods (dishwasher and washing machine) to my HEMS so that it can start washing loads at the optimum time – that is the times with the lowest projected electricity cost for a typical wash cycle.
The above image shows the prices for December 18th and 19th as downloaded by the HEMS at 16:45 on the 18th after publication of prices for the 19th. The HEMS then reviews this pricing against the need for electricity to determine when to use electricity and, in the case of the battery when to discharge the battery. I currently have the HEMS configured to provide:
3 hours of car charging
Not more than 2 hours of water heating (but only when electricity is cheaper than gas which can also heat water)
6 hours of fixed battery charging.
Best start time for dishwasher and washing machine.
I’ve described the operation of car charger, water heating, and battery previously; the new content here is the two final columns on each side for dishwasher and washing machine. The numbers in the columns are the estimated cost of a washing cycle if started at the beginning of the corresponding half-hour. The yellow colouring reflects the selected half-hour to start the appliance i.e. the one with the lowest estimated cycle cost. There’s also the option to set a threshold above which one is not prepared to pay to wash today which has resulted in the red box. If the whole day was red then the washing cycle would be deferred for consideration the next day.
The extract from the WIFIPLUG history for the dishwasher shows the typical operating sequence:
@22:19 I turn the WIFIPLUG on to enable the dishwasher to be programmed and the cycle started. The plug for the dishwasher is inconveniently at the back of a low kitchen cupboard so this was achieved via the WIFIPLUG app.
@20:20 I turn the WIFIPLUG off via the app suspending the cycle in its first moments.
@02:00 the HEMS turns the plug on remotely allowing the dishwasher cycle to continue.
The equivalents for the first two actions for the washing machine can more conveniently be achieved via the push button on the WIFIPLUG itself as the plug for the washing machine is above the counter.
The above image shows the measured power consumption from my smart meter. Almost 9 kW is being drawn at times under the action of the HEMS when the electricity price is cheapest, but also zero at times when the price is highest. The washing machine contributes to the peak spike around 02:00 when both it and the car charger are enabled. The later spiking during the peak period is the electric oven cycling on and off under control of its thermostat as the battery isn’t powerful enough to run the oven, so the excess power is drawn from the mains.
The screenshot above shows the half-hourly electricity consumption and costs from Octopus. It should be noted that this is half-hourly consumption in kWh whereas the prior chart was power so, for example, an average 6.6 kW of power consumption results in 3.3 kWh of energy consumption in a half-hour. That the blue line of price is almost the inverse of the red cost columns indicates the HEMS is doing its job to shift most energy use to when the energy price is lowest, and use the battery to offset demand when the price is highest.
Overall on the 19th I paid £1.07 for 21.776 kWh of electricity including standing charge which is independent of use. That’s 86 p for 21.776 kWh without the standing charge, or a weighted average of 3.95 p/kWh. That weighted average of 3.95 p/kWh compares to a range of 1.10 to 26.24 p/kWh during the day. According to UK power the average cost of electricity in the UK is 14.37 p/kWh so I paid 27.5% of the average UK price on December 19th.
This post describes the evolution of my HEMS code to control my dumb wet goods (dishwasher and washing machine) using smart plugs.
The program for my HEMS works as described below. For clarity I’ve emboldened the new steps associated with the control of the wet goods:
Download the cost data for Agile from Octopus. The API provides 48 hours of data, but I use only 24 hours at a time. I download at 16:45 to create a schedule from 17:00 today to 17:00 tomorrow. I use two fields only: the date-time stamp and the energy price inc-VAT.
Calculate cycle cost. Reverse sort the unit cost data in descending time, and combine the energy price with the load profile for each non-interruptible device (dishwasher and washing machine) to estimate the cost of running a washing cycle starting on each half hour. Add as third and fourth fields to the data file.
Establish start time for each non-interruptible load. For each appliance in turn, sort the cost data in ascending cycle cost. Enable the appliance for interval with the lowest cycle cost within an acceptable time window (typically the first row), and overwrite remaining instructions from the prior day. Repeat for other appliances.
Establish on times for each interruptible load. For all interruptible loads (battery, car charger and immersion heater) sort the data in ascending unit cost. Replicate the unit cost column for each interruptible load. For each load enable for the required number of half-hourly intervals within the time window set by the user, and disable for higher cost half-hourly intervals.
Prepare user screens. Sort data file by ascending time, split into first and second 12-hour periods, and present as two HTML files.
The top level scheduling script which runs automatically at 16:45 each day is a shell script which calls a series of awk scripts to: (i) calculate cycle costs for wet goods, (ii) determine start time(s) for each wet-goods appliance, (iii) determine on/off times for interruptible loads.
Awk is a pattern-matching program for processing text files. Such text files may be thought of a series of records and fields in a textual database. Awk may seem an odd choice of scripting languages, but essentially the processing of a text file of 96 time and unit pairs to create HTML files of 48 times and cost combinations is a text file processing task. Along the way as each on or off decision is made a system call to the OS is made by awk to copy an on or off script to a time-stamped script (e.g. either washingmachineon.sh or washingmachineoff.sh is copied to washingmachine_0930.sh). An internal timer called cron runs each half-hour so, for example, at 09:30 it runs all the scripts with 0930 in their names which updates the status of each controlled device.
At the time of writing we have 5 HEMS-controlled devices:
Battery storage – interruptible – the only device with 3 states (normal, charge-only, force charge).
The loads that I already control via the HEMS (that is loads which can be interrupted like battery charging, car charging and water heating) are essentially constant, that is that they draw the same power regardless of progress. The battery charging does start to tail off at particularly high states of charge, but that effect is neglected here. However the non-interruptible loads like the dishwasher and washing machine are expected to have very variable loads as the cycle continues – largely because periods of water heating demand much more power than spraying water around, spinning the drum, or pumping water out.
I thus decided that I would measure the pattern of these variable loads and use that information as part of the decision when to run the load for lowest cost. Rather than tabulate the half-hourly energy price as I do for interruptible loads, I would instead estimate the energy price to complete a load for each half hour in which I might start the cycle. This process of measuring the load pattern during a cycle I have referred to as characterisation.
I characterised the loads using a plug-in energy meter reporting kWh which I read manually every half hour during a typical wash cycle to determine the blue lines above. I then calculated the energy usage in each half hour period as per the orange line. I did all of this half-hourly as that’s the interval after which the energy price changes and correspondingly the interval at which the HEMS updates its output controls.
The measured loads confirmed my suspicions regarding variation with heat inputs providing the largest loads – twice for the dishwasher as it both heats the water at the start and heats to dry the dishes at the end, while the washing machine also heats the water at the start but spins to dry the clothes at the end (which is more energy efficient). The effect of this is that the washing machine would generally be expected to start in the cheapest half hour as that’s when it uses most energy, while the optimum time for the dishwasher is more complex to determine.
Of course the actual loads for the wash cycle will vary from the prediction for various reasons including not only the ability to select different wash cycles and options but also the smartness of the device in assessing how full it is or how dirty the utensils are.
I also decided that I would not explicitly turn these loads off from the HEMS – only turn them on. Not turning the loads off allows for some uncertainty as to the length of a cycle depending on the actual cycle selected, options selected, loading of machine etc. Not turning the outlet off also provides for the user being able to do additional loads under manual control if at home later in the day.
Internally my HEMS creates a schedule of 48 half-hourly actions each day for each load. Typically each action is either ‘on’ or ‘off’ (the fixed battery is more complex). The actions for these devices will follow a similar pattern of 48 half-hourly actions, but typically only one of those is ‘on’ and the 47 actions that would normally be ‘off’ do nothing. There existence is largely for equivalence and re-use of code, but they also ensure that the previous day’s ‘on’ command is over-written as required if the start time for the new day is different to that for the prior day.
One of the enhancements to my HEMS that I’ve had in mind for some time is to control wet goods – that is dishwasher and washing machine. I had previously thought that this might have to wait until I replaced my current machines with smart equivalents, but recently discovered that both existing machines recover after a power outage and continue their cycles. This creates the opportunity to put the machines on smart plugs, to manually start washing cycles, but then immediately turn the smart plugs off, and then use the smart plug to resume the cycle at the optimum time as instructed by the HEMS.
I already have two Eve Energy smart plugs as part of my smart heating system using Apple HomeKit, but these aren’t ideal for this application as I really need an exposed API to allow the HEMS to have control. However I then came across WIFIPLUG which, not only has an exposed API, but also Apple HomeKit compatibility (and indeed compatibility with other smart home ecosystems).
The WIFIPLUG (unlike the Eve Energy) also features an integrated on/off button allowing the washing cycle to be paused using this button, or via the HomeKit app, before later resuming under HEMS control when the energy cost is lowest.
The WIFIPLUG also has its own app which allows timers to be set and energy consumption viewed (although not in great detail) giving the ability to control via Apple’s own Home app or via the WIFIPLUG app. The one thing that I hadn’t spotted first time around is that it’s preferred to add the unit to the WIFIPLUG app (which then automatically adds to Home) whereas if you add to Home directly then you lose the ability to connect to the WIFIPLUG app later.
I was sufficiently impressed to buy a second unit even before I’d integrated the first, and indeed I now have another two on order.
A couple of times last week our dynamic electricity price excelled itself by going negative so we were actually being paid to use electricity. This situation typically arises when the weather is unusually windy causing a surplus of renewable power. Then, rather than the wind turbines being turned off to eliminate excess generation, the market price drops to encourage more consumption. Such additional consumption at the cheapest times will be a combination of genuinely increased consumption (such as my own shift from gas water heating to electric) and shifting electricity consumption from more expensive times to cheaper times (such as my own electric car charging and static battery charging).
The electricity price dropped as low as -4.85 p/kWh between 3:30 and 4:00 AM, with an average consumption-weighted unit price of 0.62 p/kWh. The red line shows the electricity price in p/kWh on the left-hand scale, the blue shows the average consumption in this billing month, and the bars show today’s consumption driven by today’s prices. (The right hand cost column is missing the leading ‘-‘ symbol where appropriate.)
The increasing electricity consumption as the price falls is driven by automated control of loads driven by my HEMS. The HEMS controls fixed battery charging (and discharging), electric car charging, and water heating in response to electricity price.
You can learn more about Octopus Agile here and save yourself an extra £50 if you decide to switch.
One of the features of the way my HEMS works is the inverse relationship between electricity demand and electricity price – electricity demand increases as the price falls and falls as the price rises. This effect helps to contribute to my low average cost for electricity – 5.63 p/kWh ex-VAT most recently (Sep/Oct 2019). The effect exists because my different controlled loads (battery, car and water heating) each need different times to achieve full (approximately 8, 5 and 2 hours respectively).
This chart is illustrative only because day-to-day needs for the different loads may change, and also the electricity price varies half-hour-by-half-hour and day-by-day but typical recent behaviour is shown. The actual rules for each device may be summarised as follows:
House baseload – support house from battery when cost greater than 10 p/kWh (limit adjusts automatically to reflect pricing on the day), otherwise take baseload from grid.
Storage battery charging – charge from the grid in cheapest 5 hours over entire 24-hour period.
Car charging – charge for a total period of so many hours within a window between arrival time at home and departure time the next day, and also any hours during day at equivalent price or lower.
Water heating – charge for the cheapest 2 hours when electricity price falls below gas price (did not occur on day illustrated below).
These same loads may also be enabled in priority order when the solar panels are in surplus. Priority is: