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    Dynamic Strategy Setup

    The dynamic strategy automates charging/discharging based on changing hourly tariffs. It is only relevant if you have a dynamic/hourly contract.

    It evaluates every price interval individually — marking the cheapest intervals to charge and the most expensive to discharge — and guarantees a minimum spread per cycle, so each marked cycle stays profitable. Because it works per interval instead of on a single fixed block, it captures both a morning and an evening price peak in the same day.

    The per-interval algorithm replaced the older fixed “cheapest N hours / expensive M hours” block approach. The deprecated block flow (02 strategy-dynamic.json) is kept in node-red/deprecated/ for reference only.

    How it works

    The strategy periodically checks for new tariff data from your supplier, marks each interval, and applies a sub-strategy per mark:

    Period Meaning Default action Configurable options
    Low (cheap intervals) Economically attractive to charge Charge (from grid) Charge, Charge PV, Self-consumption, Full stop
    Neutral (unmarked) Neither cheap nor expensive enough Charge PV Charge PV, Self-consumption, Full stop
    High (expensive intervals) Economically attractive to discharge Self-consumption Self-consumption, Sell, Charge PV, Full stop

    Smart thresholds ensure intervals are only marked when economically beneficial: a pair of intervals is only marked when its price spread exceeds the minimum delta.

    Note: the dynamic and timed strategy share settings for Charge / Charge PV / Self-consumption.

    View the explanation videos of the general idea behind this strategy:

    Solar prediction

    Tuning of charging levels (SoC) based on PV Forecast can be created by you as a user — via handmade automations or flows setting the now-available SoC and threshold fields. The project will move towards built-in integration in the future.

    Setup — getting dynamic up and running

    How to setup Dynamic

    1. Install Cheapest Energy Hours if you have not done so already.
    2. Provide data from your energy supplier to Home Assistant. See this easy list with addons from TheFes.
      • Follow any instructions provided by the Data Provider addon.
      • Tip: is your energy supplier not supported? Use ENTSO-e.
    3. Import the 02 strategy-dynamic-2.json flow into Node-RED and deploy.
    4. Go to your Home Battery Control dashboard in HA.
      • Select Full control and Dynamic to activate the strategy.
    5. Go to the Timed/Dynamic tab, this shows the timed and dynamic planning.
      • Select your energy supplier from the dropdown.
      • The dashboard should (with a small delay) display when it will be charging/idle/discharging in the next 24 hrs.
    6. Minimum price difference (min_delta)
      • Leave the price delta at €0,06/kWh or set it to your desired value.
      • For a Marstek bought at ~ €1250, 6000 cycles at 88% DoD and an 80% RTE = delta at €0,06/kWh.
      • If you charge largely using solar power, you can lower the price delta down to €0,00/kWh.

    Done.

    Need an example of a Data Provider utilizing a blueprint and template sensor? See the Nordpool (core) example below.

    Dashboard controls

    The Dynamic controls live in the Timed/Dynamic tab of the Home Battery Control dashboard.

    Control Description
    Energy supplier / Data source Selects the price data source used to determine when to charge and discharge.
    Minimum price difference (min_delta) Spread threshold in cents/kWh — pairs with a spread below this are not marked. Default 6¢/kWh (covers round-trip losses for a ~€1250 battery at 6000 cycles, 80% RTE; see the derivation in Setup). Lower towards 0 if you mostly charge from solar.
    Max cheap hours / day (cheapest_hrs) Cap on low marks per day, in hours. 0 = no limit (mark every viable pair). Useful when your battery can only absorb so much per day.
    Max expensive hours / day (expensive_hrs) Cap on high marks per day, in hours. 0 = no limit.
    Low period strategy Action during low-marked intervals. Choices: Charge, Charge PV, Self-consumption, Full stop.
    Regular period strategy Action during neutral (unmarked) intervals. Choices: Charge PV, Self-consumption, Full stop.
    High period strategy Action during high-marked intervals. Choices: Self-consumption, Sell, Charge PV, Full stop.

    Tip: the underlying Charge / Sell strategies can be configured with solar forecast, peak-shaving reserves, etc. Dynamic respects those settings.

    The price-data table (today and tomorrow with marks) and an ApexChart for review are visible in the same tab when debug/insights mode is enabled.

    Sensible starting values

    • min_delta = 6¢/kWh
    • cheapest_hrs = 0 (no cap), expensive_hrs = 0 (no cap).

    Tweaker note — how hour caps map to intervals. With 15-min price data, 1 hour = 4 intervals; the cap is rounded to whole intervals. Asymmetric caps (e.g. cheapest_hrs = 2, expensive_hrs = 1) produce asymmetric marks — a “free sell” without a paired buy is allowed when the cap suppresses one side. Net spread only deducts round-trip loss for complete pairs.

    Use-case configurations

    What the marks mean across all three configurations:

    Mark Meaning
    low Cheap interval — economically attractive to charge
    high Expensive interval — economically attractive to discharge
    null (neutral) Not marked — neither cheap nor expensive enough relative to the other

    1. Self-Consumption Maximizer

    Goal: Reduce grid imports for household loads; protect against price peaks; minimal grid interaction.

    Mark Action
    low Charge from grid
    neutral Charge PV only
    high Self-consumption (discharge to home loads, no export)
    Suggested starting values:

    min_delta ≈ 5-6¢/kWh, cheapest_hrs ≈ 2–4, expensive_hrs ≈ 4–6 — tune to your battery’s daily cycle budget.

    Caveats:

    • On sunny summer days, the battery may already be full from PV before a low interval begins. The grid-charge slot is then wasted.
    • high only saves the retail price you would have paid for grid import.
      • If PV is already covering loads during a high hour, discharging stored energy on top of that provides no additional benefit.
    • If a long stretch of high intervals occurs (cold evening, no sun), the battery can deplete before prices drop.

    2. Arbitrage Trader

    Goal: Buy cheap, sell expensive, profit on the spread. Treat the battery as a wholesale-market participant.

    Mark Action
    low Charge from grid (charge until full)
    neutral Full stop / minimal Charge PV top-up
    high Sell (until empty)

    Suggested starting values: min_delta ≈ 8–10¢/kWh (must cover round-trip losses plus some profit margin), cheapest_hrs and expensive_hrs matched to your battery’s daily energy budget so you don’t over-cycle.

    Caveats:

    • Requires a dynamic contract.
    • In the Netherlands, the salderingsregeling phase-out (from 2027) progressively reduces the feed-in value; this configuration has a limited shelf life under current law.
    • Daily cycle count increases — faster return on investment — battery degradation may also accelerate.
    • Grid-export caps and dynamic transport tariffs can claw back profit; the algorithm does not see them.
    • House loads during high hours may be drawn from the grid at high prices after the battery is discharged — this can feel counter-intuitive even when the overall math is positive.

    3. PV-First Hybrid

    Goal: Run on own solar as much as possible; only use the grid when the spread genuinely beats PV self-consumption.

    Mark Action
    low Charge from grid (solar forecast; PV on sunny days, grid otherwise)
    neutral Charge PV
    high Self-consumption (no import/export)

    Suggested starting values: min_delta ≈ 0–2¢/kWh (since PV charging is essentially free, almost any spread is profitable), cheapest_hrs ≈ 1 (avoid charging more than needed when PV is plentiful), expensive_hrs = 0 (no limit).

    Caveats:

    • Requires solar forecast to be configured. Easy, see.
    • This is the most conservative on battery wear of the three configurations — fewest grid-charge cycles — at the cost of missing some spread opportunities.

    How the algorithm works

    Under the hood — algorithm internals (for tweakers) The algorithm is called **Extreme-Pair Matching** and runs **per local calendar day** on the available price data. Per-day pairing means today's marks don't shift when tomorrow's prices arrive. **Step-by-step (per day):** 1. Take all intervals for the day and sort them by price. 2. Point to the cheapest interval (low pointer) and the most expensive interval (high pointer). 3. Calculate the spread: `price_high − price_low`. 4. If the spread is **≥ `min_delta`**: - Mark the cheap interval as `low` (unless this day's `cheapest_hrs` cap is already reached). - Mark the expensive interval as `high` (unless this day's `expensive_hrs` cap is already reached). - Move both pointers one step inward (next cheapest / next most expensive). - Repeat from step 3. 5. If the spread falls **below `min_delta`**, or both per-day caps are reached: stop. No further intervals are marked for this day. The result is a set of `low` / `high` marks where each detected pair has a guaranteed minimum spread. With `cheapest_hrs = 0` and `expensive_hrs = 0`, marks come in matched pairs — every `high` is backed by a `low`. With non-zero caps, one side of a detected pair may be suppressed, so the final mark counts can be asymmetric. The algorithm pairs by price only; it doesn't check whether the cheap interval comes before the expensive one in time. See [Current limitations](#current-limitations) for what that implies in practice. On a flat-price day where no pair meets the threshold, the strategy marks nothing — and that's the correct behavior. **Why this algorithm?** It marks the truly extreme hours (highest peaks as `high`, lowest troughs as `low`) — the intervals a user would intuitively expect. It is `O(n log n)`, straightforward to implement, and easy to explain. It does not attempt a globally optimal pairing (which would require backtracking).

    Current limitations

    The algorithm identifies economically valid candidates; it does not enforce physical feasibility. Three things it cannot see:

    • Time direction — a high interval at 02:00 cannot be served by charging at a low interval at 11:00 of the same day; the battery would have needed to be charged the previous evening. Mitigation: use Solar Forecast charge or a Timed strategy to pre-charge for early-morning high intervals.
    • Battery capacity — if 8 intervals are marked low and 8 high but the battery only holds enough for 3 charge/discharge cycles, 5 of those marks are aspirational. Mitigation: the cheapest_hrs / expensive_hrs caps directly address this — set them to your battery’s daily cycle budget.
    • PV contribution — extra charge from solar during neutral intervals can make a planned low grid-charge unnecessary. Mitigation: set Low strategy to Charge PV instead of Charge on sunny days, or use a forecast-aware automation to switch dynamically.

    Examples

    Nordpool (core)

    1. Install Nordpool Core integration (link)
    2. Install Cheapest Energy Hours integration through HACS (link)
    3. Install blueprint from Cheapest Energy Hours (link)
    4. Put the following code into your template.yaml file, make sure that file is linked from your configuration.yaml file:
    - use_blueprint:
    path: TheFes/energy_price_sensor.yaml
    input:
      entity_id: sensor.nordpool_ceh_prices
      source: nordpool
      resolution: 15
      name: Nordpool Cheapest Energy Hours
  unique_id: 881b6558-26c6-44bb-81c5-d86c05451bd4
    1. Now you have a sensor called sensor.nordpool_ceh_prices.
    2. Replace this sensor name in the Node-RED flow for the Dynamic strategy: go to Node-RED, select the Strategy Dynamic tab, double-click Data Source Settings.
    3. Probably you have to wait some time until data is loaded.

    Feedback

    Report issues, ideas, or unexpected behavior on our Discord or the project’s GitHub Issues.

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