49 lines
1.5 KiB
Markdown
49 lines
1.5 KiB
Markdown
# Optimizer
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Optimizer uses mixed integer linear programming (MILP) to minize a cost function. The optimizer model implementation honors:
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- energy consumption/ feed-in costs
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- solar forecast
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- base load (aka home) energy demand
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- end of forecast commercial value
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- 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
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- home battery or loadpoint/vehicle...
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- capacity, soc and charge goals
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- charge/discharge power limits and efficiency
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Optimization spans N slots with N being minimum of available forecast data.
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## Parameters
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### Energy consumption/ feed-in costs/ Solar forecast
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Grid/ feed-in/ solar tariff data.
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TODO
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- [ ] make feed-in optional
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- [ ] make solar optional
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### Base load energy demand
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Collected 15min energy profile averaged over the last 30 days.
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### Measured value blending
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The solar forecast and the base load profile are anchored to the current situation
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using the last completed 15min metrics slot, decaying linearly over 4 slots:
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- base load: the measured home consumption replaces the first slot and decays into the profile
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- solar: the scale factor measured production/forecasted production is applied to the first slot and decays towards 1
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### End of forecast commercial value
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Use minimum of energy consumption cost.
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### Home Battery
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### Loadpoint and Vehicles
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- home battery or loadpoint/vehicle...
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- capacity, soc and charge goals
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- charge/discharge power limits and efficiency
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