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feat(optimization): model battery LCOS and probabilistic bypass
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@@ -145,6 +145,11 @@ The energy management can be run in three modes:
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`prediction.hours * (3600 / interval)`, and device power caps as well as the solution
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and energy-management-plan serializers are slot-aware.
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- **terminal_value_euro_per_kwh** (`float`, default: `0.0`): Monetary value assigned to usable
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battery energy remaining at the end of the optimization horizon. This terminal value influences
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whether the optimizer preserves or depletes the battery near the horizon. It is independent of
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the battery's `levelized_cost_of_storage_kwh`, which prices actual discharge throughput.
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:::{note}
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Use `900` together with a 15-minute electricity price source (for example a dynamic or
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exchange-priced tariff) to let the optimizer schedule on a quarter-hour grid. Keeping the
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@@ -224,8 +229,9 @@ The behavior of the genetic algorithm can be customized using the following conf
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```json
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{
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"optimization": {
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"hours": 24,
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"horizon_hours": 24,
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"interval": 3600,
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"terminal_value_euro_per_kwh": 0.20,
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"genetic" : {
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"individuals": 300,
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"generations": 400,
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@@ -342,6 +348,19 @@ The inverter supports separate AC↔DC conversion efficiencies:
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}
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```
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`levelized_cost_of_storage_kwh` is an optional variable cost in EUR/kWh (default `0.0`). EOS applies
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it exactly once to the DC energy actually delivered by the battery, whether that energy supplies
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the local load or is exported to the grid:
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```{math}
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C_{LCOS} = \frac{E_{bat,out,Wh}}{1000}\,c_{LCOS,EUR/kWh}
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```
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Charging energy, battery-internal discharge losses, and the subsequent DC-to-AC inverter loss do
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not receive another LCOS charge. LCOS is included in the hourly and total simulation costs and is
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separate from `optimization.terminal_value_euro_per_kwh`, which values only the usable energy left
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at the end of the optimization horizon.
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#### Home appliance simulation configuration
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**Example:**
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