HEM Deep-Dive: Energy Flex
HEM engine v2.00a2 (Python); FHS v1.00a7.
At time of writing, HEM is still in development and not yet approved for regulatory compliance.
You guys need to stop encouraging me with these weekly Home Energy Model posts! 🤣 But before an Intervention group is formed, here's another one.
These charts show three 48-hour snapshots of a small house, built to the Future Homes Standard, except it has direct heating (panel heaters) and direct electric immersion (instead of a heat pump).
The model includes a PV array (4kWp), a 5 kW battery, and an immersion diverter.
The graphs show a forecast of the dwelling's Energy Flex - when the dwelling is importing electricity, exporting, charging the battery, using the battery, and diverting to the hot water cylinder.
As more homes are designed to include PV, batteries and EV chargers, understanding how they use electricity, and when they're most likely to be importing and exporting power, is becoming a crucial piece of the puzzle for energy supplies and network operators to understand.
This combination of technologies is impossible to assess in SAP, partly because SAP uses monthly average assumptions, but also because SAP lets you choose a battery, or an inverter, but never both!).
HEM does allow both, and lets the assessor prioritise which is topped up first. It doesn't consider EV charging yet, but may do in a future release.
These graphs show three 48 hour periods of energy use for the same dwelling. I've taken snapshots in February, July and October, but the HEM model includes this data for the entire year.
There's a lot of pretty colours in these charts..
Top bar: Grey and yellow = Symbolising night and day.
Red = Dwelling is importing electricity from the grid.
Light green = The PV is being used instantaneously within the dwelling.
Yellow = The dwelling has an excess of power from the PV. It's topping up the hot water cylinder (Diverter).
Light blue = The dwelling has an excess of power from the PV. It's charging its battery.
Dark blue = The dwelling is using its battery.
Dark Green = Excess electricity from the PV is being exported to the grid.
July (Graph 2) shows small blasts of importing (red) where the immersion is being topped up. For the rest of the day the PV is providing more than enough electricity to meet the needs of the occupant, using excess power to charge the battery (light blue) and keep the hot water hot (yellow) before exporting everything else to the grid.
October (Graph 3) shows higher import levels, as some heating is required. Only a tiny fraction of onsite generation is being exported - most of the energy from the PV is used instantaneously in-house (light green) or to charge the battery (light blue) which then mostly discharges in the evening (dark blue).
February's imports (Graph 1) are off the chart (due to reliance on panel heaters). Onsite generation is very low with none being exported. The battery takes priority over the diverter, and there isn't enough coming from the PV to feed both.
Why do we need such detail?
Scaling this up, HEM data will be able to predict the energy demand of an entire housing development, forecasting peak import demand over winter and peak export demand over summer.
This will be more than useful info for network operators when there are concerns about loads on the infrastructure.
It's also great data for energy suppliers, nerdy homeowners, and is an important piece of the puzzle for making EPCs and running cost predictions more accurate.
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