Author: Jonathan

  • Optimum setback temperatures for heat pumps

    Heat pump owners who have previously been used to gas or oil may be tempted to set their thermostats down at night or when not at home. That can be a costly mistake. Here’s why.

    Heat pumps are most efficient when the radiators they heat are slightly warmer than target room temperature, when they feel barely warm (i.e. around 30oC).

    But the amount of heating power from radiators at these low temperatures is less than from hot radiators. For this reason, heat pumps often run almost semi-continuously in winter.

    The further the temperature is set back, the higher the heat pump must bring the radiators to restore a house to its target temperature within a desired period. That period is typically 2 to 6 hours and can be changed indirectly by adjusting a heat pump’s weather curve setting.

    But there is another even more important reason: off-peak times are the best times for spacing heating anyway.

    Altogether, several factors influence the optimum set back temperature, including:

    • house thermal inertia and how well insulated it is. For example, timber frame homes warm up more quickly the brick homes and allow lower set back temperatures.
    • heat pump’s energy efficiency and its maximum power output;
    • electricity tariff;
    • when and for how long the house is occupied and the value attached to room temperature being on target at these times.

    Optimum set back temperatures are not easy to calculate by hand but are returned in an e-mail by the energy simulator automatically as an e-mail message: Optimum winter heat pump setback temperature for this configuration is xxC.

    To obtain optimum set back temperature, include a heat pump in your simulation and quantify the following parameters in “location”:

    • “temperature_target_celsius”: target room temperature;
    • “target_hours” is an array comprising the hours in the day (e.g. 0 for 00h00m – 00h59m, 1 for 01h00m-01h59m) when the home is to be heated to target temperature;
    • “temperature_half_life_days” is the time in days, when heating is switched off completely, for room temperature to fall exactly half way between target and external ambient temperature: best measured on a cold winter day;
    • “intolerance_gbp_per_deg_c_per_hour” is the notional cost of inconvenience arise from the room temperature not being at its target value.

    For example:

    "location": {
   . . .
    "internal" : {
      "temperature_target_celsius": 21.0,
      "temperature_half_life_days": 1.5,
      "target_hours": [8,9,10,11,12,13,14,15,16,17,18,19,20,21],
      "intolerance_gbp_per_deg_c_per_hour": 0.1
    }
  },

    Don’t be surprised to receive an e-mail response that the set back temperature should be the same as (or even slightly higher than) your target temperature: normal for well insulated brick/masonry homes with off-peak tariffs.

  • Radiators and efficiency

    When people consider installing a heat pump, the spotlight often falls on it only: it’s brand, technology, refrigerant, and advertised efficiency. However, the major factor determining how efficient a heat pump will be is the radiators it is connected to.

    This is because a heat pump’s seasonal coefficient of performance (SCOP) — a measure of how much heat energy it can deliver compared to the electricity it consumes across a whole year — is highly sensitive to the temperatures it has to work at. Radiators directly dictate those temperatures. If the radiators are undersized, inefficient, or in poor condition, the heat pump will be forced to run hotter, and its SCOP will tumble.

    Radiators and Flow Temperature

    Radiators don’t produce heat; they “transfer” it. The larger the radiator surface area and the more efficient its design, the more heat it can emit at a given water temperature.

    • A modern double-panel, double-convector radiator might emit the same heat at 45 °C water temperature as an older single-panel radiator would at 65 °C.
    • If your home has lots of small, old radiators, the only way they can deliver enough warmth in mid-winter is if the water flowing through them is hot — often 60–70 °C. That’s fine for a gas boiler, which happily produces hot water at 70–80 °C, but a heat pump’s efficiency plummets when pushed above 50–55 °C.

    The simplest way to allow a heat pump to run efficiently is to have more radiators. Each additional radiator adds emitting surface area, meaning the system can deliver the same heat to the home while running at a lower water temperature.

    For example:

    • Suppose a room needs 2 kW of heat on a cold day.
    • One old single-panel radiator might only emit 2 kW at 65 °C water.
    • Replace it with two modern radiators, each sized to deliver 1 kW at 45 °C, and the heat pump can now run at 45 °C flow instead of 65 °C. That could boost SCOP by 20–40% depending on climate.

    Distribution across rooms

    It’s not just total number that matters, but how they’re distributed. An oversized radiator in the living room won’t help if the bedrooms upstairs are freezing because they still have tiny 600 mm singles. Heat pump efficiency depends on being able to keep the whole home comfortable without raising flow temperature for one weak link.

    Floor area vs radiator count

    Larger homes naturally need more radiators. But many UK homes built in the 1960s–1990s were fitted with minimal radiator counts — just enough to keep a gas-boiler system adequate. Retrofitting for a heat pump often means increasing radiator numbers by 20–50%.

    Panel size and surface area

    The physical size of a radiator is the single most obvious determinant of its output at a given water temperature. Tall, wide, or deep radiators simply have more surface for air to contact.

    • Single-panel radiators are the least powerful per length.
    • Double-panel, single-convector (P+ type) add output.
    • Double-panel, double-convector (K2 type) can deliver nearly three times the heat of a slim single.
    • Triple-panel designs (K3) can achieve very high outputs at low temperatures, though they are bulky.

    Low-temperature efficiency

    Because heat pumps thrive at lower water temperatures, radiators need to be oversized relative to what a gas boiler required. A room that previously had one P+ might need a K2 or even two radiators at 45 °C design flow.

    Vertical radiators

    These have become fashionable, but many have less surface area than horizontal radiators of the same height. Unless carefully specified, they can be a liability for heat pump SCOP.

    Radiator Efficiency Beyond Size

    It’s not only surface area that matters. Design tweaks influence how effectively a radiator converts hot water into room heat.

    • Convection fins: Modern convector plates welded between panels greatly boost airflow and heat transfer.
    • Airflow patterns: Radiators placed under windows use rising warm air to counteract downdraughts; when moved to less effective positions, their practical efficiency drops.
    • Active flow: Adding very low power fans to radiators increases airflow over a radiator’s surface at a negligible running cost. This enhances heat transfer by boosting the convection effect, helping distribute heat more evenly and quickly, even with lower water temperatures.
    • Radiant vs convective balance: Most radiators are largely convective (heating air), but designs like cast iron or aluminium can emit more radiant heat, making rooms feel warmer at lower air temperatures.

    High-efficiency radiators allow the heat pump to run cooler and keep SCOP higher.

    The Condition of Radiators

    Even the best-sized radiator can underperform if it’s in poor condition.

    Sludge and corrosion

    • Over years, central heating systems accumulate sludge (magnetite particles) and rust.
    • This reduces water flow, creates cold spots, and slashes radiator output.
    • A sludged-up radiator might deliver 20–40% less heat than its rating, forcing the heat pump to raise flow temperature to compensate.

    Air trapped in radiators

    Trapped air pockets reduce effective surface area. Regular bleeding is essential.

    Fouling of fins

    Dust clogging the convector fins under radiators impedes airflow and reduces output. A quick vacuum can measurably improve performance.

    Valve performance

    Old radiator valves may not open fully, restricting flow. Poor hydraulic balancing leads to some radiators running too cool while others hog flow, again pushing up system temperatures unnecessarily.

    Keeping radiators clean, flushed, and balanced is a surprisingly powerful lever on SCOP.

    Interaction with Heat Pump Control Strategy

    Radiators and controls work together.

    • Weather compensation: A heat pump can automatically vary flow temperature based on outdoor temperature. With adequately sized radiators, weather compensation keeps flow very low in spring/autumn, giving spectacular COP figures.
    • Constant low-flow operation: Radiators designed for continuous low-temperature heat work best with heat pumps. Stop-start control or undersized emitters undermine this.
    • Zoning pitfalls: Shutting down too many radiators (e.g. only heating one room) can reduce flow volume through the system, making it harder for the heat pump to modulate efficiently.

    Practical Examples of Radiator Impact on SCOP

    Let’s take a worked example to see how radiator capacity alters SCOP.

    • A typical 3-bed UK semi requires ~8 kW peak heat load at −3 °C outdoor temperature.

    Case A: Old boiler radiators

    • Existing system: 7 single-panel radiators sized for 70 °C flow.
    • At 45 °C flow they can only emit 4 kW.
    • Heat pump must run at 60 °C+ to keep house warm.
    • Average SCOP over winter: ~2.7.

    Case B: Upgraded radiators

    • Replaced with 11 double-panel convectors sized for 45 °C design.
    • Heat pump runs at 45 °C most of winter.
    • Average SCOP: ~3.8–4.0.

    Case C: Oversized radiators

    • 14 radiators, all K2 or K3 types.
    • Design temperature 35 °C.
    • Heat pump SCOP: 4.5–4.7, with some days reaching instantaneous COP above 5.

    A difference in running cost between Case A and Case C could be £400–£600 per year for the same house, plus lower CO2 emissions.

  • Finding the best tariff

    Heat pumps in winter

    Heat pumps are most efficient when they heat radiators to just above room temperature. This requires them to run almost continuously with result that most electricity consumption is during standard or peak times.

    While storage batteries give access to off-peak electricity, conventional tariffs with a single early morning slot are not ideal because the duration to the next slot is too long. A heat pump for a 4 to 5 bedroom running on a cold day consumes about 2kW – 4kW, and would drain a decent sized 13.5kWh battery by midday. Tariffs with multiple off-peak slots per day are prefered because they shorten the discharge periods.

    Heat pumps such as Octopus Energy’s Cosy tariff (above) allow batteries

    Solar pv

    Conversely in spring and summer, houses with solar pv and batteries prefer high export rates.

    With the steady growth in solar generation capacity there is an over supply of electricity.

    An oversupply is developing in the middle of the day which drives down export prices, but which peak in the evening as the sun goes down: the so called “duck curve“.

    Octopus Energy’s Flux export tariff, for example, currently pays 29p/kWh between 4 and 7pm in our area: compared to only 10p/kWh or less at other times.

    Octopus Energy’s Flux tariff

    The result is that the economic benefit of solar generation is progressively reducing unless one can either use it or store it for use export later in the day.

    Avoid generalisations

    Being on the wrong tariff can result in significant and unnecesary penalties – but do not rely on bloggers or suppliers’ marketing to tell you what tariff is best for you. No two homes are alike. Seasons and local weather complicate things further, especially if you have a heat pump or solar pv.

    In particular, “heat pump” tariffs do not automatically make sense for homes with heat pumps. In our our case, we are about £1,400pa better off on Flux Import/Export compared to Cosy: this is because we have a comparatively large 40x panel solar pv array.

    Switching tariffs during the year fine tunes things further: in our case, being on Cosy during the very coldest and darkest months of December and January, reduces electricity costs further by approximately £200pa.

    Simulate to accumulate

    While being on the optimum electricity tariff can result in big savings, finding it is not easy.

    Many home owners are guided by the data from their home energy dashboards, using daily costs before and after switches to confirm their decisions. This is an unreliable because many things like weather, seasonal changes and consumption patterns changes can mislead interpretation of the results.

    A better approach is to simulate tariff options to find the best for your case. To do this, put the tariff possibilities into “energy” tag, set “max_project_duration_years” to 1 year and pick the tariff whose simulation gives the best NPV.

    Nevertheless, there is a limit to how accurate simulators can be. By contrast, home energy controllers measure real data in real time and so, for this reason, I wrote tariff selection into our battery controller. It reviews the tariffs daily and e-mails us when something better is found.

    Some energy providers discourage frequent tariff changes: so choose your supplier and switches accordingly.

  • Our battery optimiser

    I wanted:

    • a stripped down, dedicated controller that is as reliable as the system it controls;
    • to improve accuracy by correlating heat pump power consumption with outside temperature: both measured by our EmonCms energy monitoring system;
    • to use local temperature forecasts from the MetOffice to better predict heat pump energy consumption (rather than relying on solar generation forecasts alone);
    • to factor in latent costs due to battery wear;
    • use best in class optimisation methods to allow for constantly changings forecasts for the coming day and, simultaneously, minute-by-minute net consumption changes due to passing clouds and fluctuating loads.

    So I extended the planning tool to include an optimiser that runs on a Raspberry Pi.

    Our battery optimiser: written in PHP and MySQL, and hosted over Ubuntu on a Raspberry Pi5 (16GB RAM, 1TB M.2 SSD), networked and powered across Ethernet PoE

    You are welcome to try and use it. Just remember that it will require hosting on your LAN and currently controls GivEnergy AIO batteries only1 and relies on an EmonCms energy monitoring system (another Raspberry Pi) for some data.

    The optimiser requires only occassional monitoring and has been controlling our GivEnergy AIO battery for several weeks now. Below is an excerpt from its web dashboard:

    When the solution is zero import/export2 the optimiser leaves control to the battery. When the solution requires import or export, it takes over and controls the battery’s charge/discharge rate minute by minute, based on cost optimisations using real time and historical inputs from:

    1. Caveat emptor: no guarantees that it will work for you … or that it won’t burn your house to the ground! ↩︎
    2. I.e. the house is powered entirely by the battery plus solar, if any. ↩︎

  • Battery optimisers

    As home storage batteries reduce in cost, they are becoming a popular means to reduce electricity costs, by:

    • storing generated solar energy for later consumption, in place of exporting it to the grid and later having to import it for consumption at a higher price.;
    • importing energy at off-peak times and re-exporting the same back to the grid at higher peak prices (so called “arbitrage”).

    Batteries allow users to set timers to charge and discharge at particular times of day. Many users set them to charge during off-peaks times and discharge during peak times. This way, to the extent that batteries have sufficient capacity, users benefit from off-peak rates at other times, and even make profitable trades at peak times.

    In practice, however, there are many problems with using timers alone:

    • constantly changing daily solar generations change the amount of off-peak import energy that is needed: if you import too much, you have spent too much;
    • evening and early morning consumptions (e.g. heating during the winter) make exports unprofitable because energy has to be re-imported;
    • variable rates (e.g. Octopus Agile’s rate changes every 30 minutes) are complicated and constantly changing;
    • wear costs from over or under charging, or from overheating caused by rapid charging, can exceed grid savings;
    • differences between peak export and off peak import rates may not be sufficient to justify charge-discharges on some days, especially when wear is considered;
    • fluctating solar generation require the battery charge/discharge rates to vary with net house load (i.e. after solar generation) to prevent the battery:
      • charging from the grid during off peak times when clouds are overhead;
      • discharging to the grid during peak times, when excess solar generation is otherwise sufficient;
    • optimal tariffs vary throughout the season, requiring time consuming changes to timer settings.

    Automated real time controllers can minimise these issues and save several hundred pounds or more a year in electricity costs over using fixed timers.

    Battery features

    Some batteries have features that help optimise control to an extent. For example GivEnergy’s ECO mode charges when solar generation exceeds demand, and discharges to meet demand when solar is insufficient. However, it does not adapt to your consumption patterns or speculatively arbitrage the grid according to weather forecasts and changing tariffs.

    Other “smart” features allow energy suppliers to take control of the battery to charge and discharge automatically. But looking at users’ comments, these appear not to dynamically control the battery in response to changing weather, consumption and forecasts. That would probably require a lot of computation. And why would a supplier do so if the end result is to trim customers’ bills? Possibly for the same reasons, these smart features do not advise when another tariff makes more sense.

    Subscription services

    MyEO and smug offer web subscription services to do this for ranges of batteries and tariffs using solar forecasts. However, at the time of study, I did not find disclosures on methods or whether actual weather forecasts and outside temperature measurements are used: the two most reliable predictors of energy consumption. The services are further not free, which all makes it difficult to confirm whether the benefits outweigh the subscription costs.

    Home Assistant

    Other optimisers use the Home Assistant (HA) open source home automation platform with plugins. HA is an excellent platform, runs locally and can be hosted on low cost Raspberry Pi’s. One plugin example is GivTCP for GivEnergy batteries which can use free domestic solar forecasts from Solcast. Another plugin Predbat supports additional brands including Fox, Sigenergy and SolarEdge. However HA’s open community integrations may not always work reliably, especially when combined with other plugins.

    Dedicated controllers

    From what I can see, no optimiser has yet emerged that works with all the major battery brands, learns from home consumption patterns and sensor data, uses both solar and weather forecasts and works with all the major energy provider tariffs.

    It would be nice if web service did all this, and I am sure this will happen. Meanwhile, I extended our simulator to run as a dedicated real time optimising controller for our case.

  • Control

    Renewable systems planning doesn’t end with installation.

    More work is needed to:

    • control storage batteries, if they are included, are expensive and if used to the wrong extent waste the investment put into them;
    • control other energy storage appliances, such as when to heat the hot water cylinder – and when to heat the house;
    • stay on the right tariff.

  • Maximising your solar

    If you’re adding solar PV, it’s important to get the best possible financial return on your investment by maximising the amount of power generated across the year:

    • use the maximum solar pv area1 possible: your installer should advise. South facing is best and, in order of suitability, this is usually:
      • pitch roofs get most sun and make best use of space because panels can be butted together;
      • flat roofs make less efficient use of area because they require spacings between panel arrays and may require planning permissions, then;
      • ground installation: if you (and your neighbours) can live with them.
    • if you have sufficient panel area to generate more than the 3.68kW export limit, get your installer to make a G99 application to your DNO to get the limit raised. The application is often free and will allow your installers to fit a bigger pv system;
    • oversize your solar pv generation capacity to the maximum “oversize” limit permitted for the inverter you are using: this is usually between 130 and 150 percent of its maximum power output. This allow your inverters to run safely at their maximum output for a greater proportion of the time.
    1. Subject to your inverter capacity and G98 or G99 export limit. ↩︎

  • Requesting my server

    If you don’t want to install the simulator on your server, try mine. It’s up 24/7, but no promises of course concerning either its availability or accuracy.

    Solve a puzzle

    To keep the bots out, my server expects a “token”: the name of the French physicist who discovered the heat pump effect in the early nineteenth century. Google to find his or her full name, form a non-accented character string <token> from it in lower case without spaces and add it as a top level element to your request:

    {
  . . .
  "token": "<token>",
  . . .
}

    To receive an e-mail when your results are ready, add your e-mail address <email> as a top level element as follows:

    {
  . . .
  "email": "<email>",
  . . .
}

    Make a request

    POST your json request to:

    https:://renewable-visions.com/wp-json/api/projection

    You should receive a json response immediately, telling you whether your request has succeed or failed.

    Fixing errors

    If your request has an error, the server will respond with 400 Bad request and a json diagnostic, for example:

    {
    "message": "'storage_hot_water' component is missing"
}

    Keep fixing your errors your request is accepted and you see .

    Successful request

    If your request is successful, the server will respond 201 Created and a message with the url to your results:

    {
    "message": "Get your result at <url>. Will e-mail you when ready at <email>.",
    "request": {
        ....
     }
}

    The response also contains a copy of your request. If you omitted optional parameters, it will include assumptions made in their absence.

    Simulations can take several minutes to run, or longer if the server has a queue of requests.

  • Avoid lone heat pumps

    Replacing boilers with heat pumps alone makes no financial sense with today’s energy prices, even after the £7,500 BUS grant.

    Electricity’s (currently 25.7p / kWh) 4x price premium over oil and gas (6.3p / kWh) exceeds their efficiencies1 relative to the boilers they replace, and makes them more expensive to operate. Worse, they may never repay their capital costs.

    There’s a lot of hype about how batteries allow heat pump owners to benefit from off peak prices. The maths says differently: get a battery solely for a heat pump and you are simply digging a bigger hole.The battery will never pay, whatever the duration.

    Our house: viability of different combinations of renewables relative to staying with our 2 year old oil boiler

    Make things sunnier

    Adding solar PV to a heat pump is like sprinkling instant sunshine, and can transform the economics of heat pumps.

    Daily solar generation for our home across the year

    In winter, solar generation is around 10-20 percent of summer levels. This is still a significant contribution because each “free” kWh of solar generated is 1kWh less imported from the grid. And come spring or autumn, there may be enough solar generated to power the heat pump entirely.

    We are fortunate to have been able to add a relatively large number of panels (40) compared to the 10kW capacity of our heat pump which, even in the depth of winter, translates on average to about 30kWh of “free” thermal energy daily.

    We’d still have been better off with PV on its own, keeping the boiler. But now heat pumps, and a CO2 free world, does make more sense than a boiler on its own – and so we got one.

    If you are considering a heat pump2, maximize solar capacity, and consider battery storage if you’re in for the (very) long run.

    1. Relative efficiency is the heat pump SCOP divided by the efficiency of the oil boiler. In our case this was 4.0 (3.62 / 0.9). ↩︎
    2. Some housing types may never justify heat pumps on financial grounds. Passive houses, for instance, have such low heating requirements that cheap electric storage heaters on low green off-peak tariffs can be more attractive.
      ↩︎

  • “demands”

    The simulator needs the following annual energy demands:

      "demands": {
    "space_heating_thermal": {
      "type": "climate_heating",
      "total_annual_kwh": 17855,
      "hourly_consumption_weightings": {
        "0" : 0,
        "6" : 1,
        "8" : 0,
        "16": 1,
        "22": 0
       },
      "target_circadian_phase_lag_hours": 3
    },
    "hot_water_thermal": {
      "type": "fixed",
      "total_annual_kwh": 2333.0,
      "hourly_consumption_weightings": {
        "0": 0,
        "7": 2,
        "8": 0.1,
        "22": 0
      }
    },
    "non_heating_electric": {
      "type": "fixed",
      "total_daily_kwh": 10,
      "hourly_consumption_weightings": {
        "0": 0.5,
        "7": 2,
        "21": 1,
        "23": 0.5
      }
    }
  },

    The annual total demands are in your EPC certificate:

    • “space_heating_thermal” is for room heating: state the hours of the day when your heating starts and when it stops. For example “0” : 0, “6” : 1, “8” means start the day with no heating, switch on at 6am, then off at 8am. Set “target_circadian_phase_lag_hours” to up to 6 hours according to how well your house is insulated. Use the “climate_heating” model to estimate heating power from “total_annual_kwh” and time of day and year;
    • “hot_water_thermal” is for heating hot water: state weightings for hot water usage. For example, “0”: 0.5, “7”: 2, “21”: 1 means a 0.5 relative weighting from midnight, until 7am when weighting increases to 2 until 9pm when it reduces to 0.5. Use the “fixed” model to estimate heating power from “total_annual_kwh” and these weightings.
    • “non_heating_electric” is daily electricity consumption excluding space and water heating (e.g. cooking, fridge, lighting, TV): best obtained by deducting from your electricity bill. As for hot water, use the “fixed” model and hourly weightings to allocate relative power according to time of day. For example, “0”: 0.5, “7”: 2, “21”: 1, “23”: 0.5 means a 0.5 weighting between midnight and 7am, then a 2 weighting until 9pm, then reducing to 0.5.