nordpool_diff custom component for Home Assistant

Requires https://github.com/custom-components/nordpool

Nord Pool gives you spot prices, but making good use of those prices is not easy. This component provides various algorithms whose output can be used for deciding when to turn water heater or car charger on/off, or for adjusting target temperature of a heater so that it will heat more just before prices will go up (to allow heating less when prices are high), and heat less just before prices will go down.

Apart from potentially saving some money, this kind of temporal shifting of consumption can also save the environment, because expensive peaks are produced by dirtier energy sources. Also helps solving Europe's electricity crisis.

Installation

Option 1: HACS

Go to HACS -> Integrations
Click the three dots on the top right and select Custom Repositories
Enter https://github.com/jpulakka/nordpool_diff as repository, select the category Integration and click Add
A new custom integration shows up for installation (Nordpool Diff) - install it
Restart Home Assistant

Option 2: Manual

Install and configure https://github.com/custom-components/nordpool first.
Copy the nordpool_diff folder to HA <config_dir>/custom_components/nordpool_diff/
Restart HA. (Skipping restarting before modifying configuration would give "Integration 'nordpool_diff' not found" error message from the configuration.)
Add the following to your configuration.yaml file:
```
sensor:
  - platform: nordpool_diff
    nordpool_entity: sensor.nordpool_kwh_fi_eur_3_095_024
```
Modify the nordpool_entity value according to your exact nordpool entity ID.
Restart HA again to load the configuration. Now you should see nordpool_diff_triangle_10 sensor, where the triangle_10 part corresponds to default values of optional parameters, explained below.

Optional parameters

Optional parameters to configure include filter_length, filter_type, unit and normalize, defaults are 10, triangle, EUR/kWh/h and no, respectively:

sensor:
  - platform: nordpool_diff
    nordpool_entity: sensor.nordpool_kwh_fi_eur_3_095_024
    filter_length: 10
    filter_type: triangle
    unit: EUR/kWh/h
    normalize: no

unit can be any string. The default is EUR/kWh/h to reflect that the sensor output loosely speaking reflects change rate (1/h) of hourly price (EUR/kWh).

filter_length value must be an integer between 2...20, and filter_type must be either triangle, rectangle, rank or interval. They are best explained by examples. You can set up several nordpool_diff entities, each with different parameters, plot them in Lovelace, and pick what you like best. Here is an example:

Triangle and rectangle

filter_type: triangle and filter_type: rectangle are linear filters. They apply non-causal FIR differentiator¹ to spot prices, resulting in a predictive sensor that gives positive output when the price of electricity for the current hour is cheaper compared to the next few hours (and negative output in the opposite case).

For illustrative purposes, the following FIRs have been reflected about the time axis; the first multiplier corresponds to current hour and the next multipliers correspond to upcoming hours.

Smallest possible filter_length: 2 creates FIR [-1, 1]. That is, price for the current hour is subtracted from the price of the next hour. In this case filter_type: rectangle and filter_type: triangle are identical.

filter_length: 3, filter_type: rectangle creates FIR [-1, 1/2, 1/2]

filter_length: 3, filter_type: triangle creates FIR [-1, 2/3, 1/3]

filter_length: 4, filter_type: rectangle creates FIR [-1, 1/3, 1/3, 1/3]

filter_length: 4, filter_type: triangle creates FIR [-1, 3/6, 2/6, 1/6]

filter_length: 5, filter_type: rectangle creates FIR [-1, 1/4, 1/4, 1/4, 1/4]

filter_length: 5, filter_type: triangle creates FIR [-1, 4/10, 3/10, 2/10, 1/10]

And so on. With rectangle, the right side of the filter is "flat". With triangle, the right side is weighting soon upcoming hours more than the farther away "tail" hours. First entry is always -1 and the filter is normalized so that its sum is zero. This way the characteristic output magnitude is independent of the settings.

Normalize

With linear filters filter_type: triangle and filter_type: rectangle, magnitude of output is proportional to magnitude of input = price (variations) of electricity. Between 2021-2022, that has increased tenfold, so the characteristic output magnitude of the filter has also increased tenfold. That causes problems in proportional controllers; if a heater target used to be adjusted roughly +-2 deg C, it's not reasonable for that to become +-20 deg C, no matter how the electricity prices evolve.

To compensate for that, normalize was introduced. Current options include normalize: no (no normalization, default), normalize: max (output of the filter is divided by maximum price of the next filter_length hours), and normalize: max_min (output of the filter is divided by maximum minus minimum price of the next filter_length hours). These work reasonably when filter_length is 10 or more, making the output magnitude less dependent of current overall electricity price. And might fail spectacularly if price or its variation is very low for long time.

Rank and interval

With filter_type: rank, the current price is ranked amongst the next filter_length prices. The lowest price is given a value of 1, the highest price is given the value of -1, and the other prices are equally distributed in this interval.

With filter_type: interval, the current price is placed inside the interval of the next filter_length prices. The lowest price is given a value of 1, the highest price is given the value of -1, and the current price is linearly placed inside this interval.

If the current price is the lowest or highest price for the next filter_length prices, both filter types will output 1 or -1, respectively. If the next three prices are 1.4, 1 and 2, the rank filter will output 0 and the interval filter will output 0.2.

Since the output magnitude of the rank and interval filters are always between -1 and +1, independent of magnitude of price variation, it may be more appropriate (than the linear FIR filters) for simple thresholding and controlling binary things can only be turned on/off, such as water heaters. normalize setting has no effect on rank nor interval.

Attributes

Apart from the principal value, the sensor provides an attribute next_hour, which can be useful when we're close to hour boundary and making decisions about turning something on or off; if it's xx:59 and the principal value is above some threshold but the next hour value is below the threshold, and we would like to avoid short "on" cycles, then we maybe shouldn't turn the thing on at xx:59 if we would turn it off only after 1 minute. This can be avoided by taking the next hour value into account.

Fancy way of saying that the price for the current hour is subtracted from the average price for the next few hours. ↩

robsonke/nordpool_diff