# Optimizer

Optimizer uses mixed integer linear programming (MILP) to minize a cost function. The optimizer model implementation honors:

- energy consumption/ feed-in costs
- solar forecast
- base load (aka home) energy demand
- end of forecast commercial value
- strategy- either "charge before export" (charge loads as soon as possible) or attenuating grid peaks on the demand side, the feed-in side or both
- home battery or loadpoint/vehicle...
  - capacity, soc and charge goals
  - charge/discharge power limits and efficiency

Optimization spans N slots with N being minimum of available forecast data.

## Parameters

### Energy consumption/ feed-in costs/ Solar forecast

Grid/ feed-in/ solar tariff data.

TODO

- [ ] make feed-in optional
- [ ] make solar optional

### Base load energy demand

Collected 15min energy profile averaged over the last 30 days.

### Measured value blending

The solar forecast and the base load profile are anchored to the current situation
using the last completed 15min metrics slot, decaying linearly over 4 slots:

- base load: the measured home consumption replaces the first slot and decays into the profile
- solar: the scale factor measured production/forecasted production is applied to the first slot and decays towards 1

### End of forecast commercial value

Use minimum of energy consumption cost.

### Home Battery

### Loadpoint and Vehicles

- home battery or loadpoint/vehicle...
  - capacity, soc and charge goals
  - charge/discharge power limits and efficiency