Smart Optimization

Smart Optimization

PowerSync includes a built-in linear programming (LP) optimizer that calculates the optimal battery charge/discharge schedule based on electricity prices, solar forecasts, and load patterns. No external dependencies required.

Acknowledgement: The optimization approach was inspired by HAEO (Home Assistant Energy Optimizer).

How It Works

The optimizer uses scipy's HiGHS LP solver to solve a cost minimization problem over a 48-hour horizon:

Minimize: Sum (import_price[t] * grid_import[t] - export_price[t] * grid_export[t]) * dt

Subject to:
  - Power balance: solar[t] + grid_import[t] + battery_discharge[t]
                  = load[t] + grid_export[t] + battery_charge[t]
  - SOC dynamics: soc[t] = soc_0 + Sum(charge*eff - discharge/eff) * dt / capacity
  - SOC limits: backup_reserve <= soc[t] <= 1.0
  - Rate limits: charge <= max_charge_kw, discharge <= max_discharge_kw

The optimizer runs directly inside PowerSync:

1. Collects price, solar, and load forecasts from configured providers
2. Overlays EV charging plans into the load forecast (if EV integration is enabled)
3. Solves the LP problem in a background thread (typically < 1 second)
4. Maps the solution to battery actions (charge, discharge, idle, self-consumption)
5. Executes battery commands via the appropriate control method

If scipy is unavailable, a greedy fallback optimizer runs instead.

Action Model

Action	What It Does	When It's Used
CHARGE	Force charge battery from grid	Cheap import periods (overnight off-peak)
EXPORT	Force discharge battery to grid	Expensive export periods (evening peak)
IDLE	Hold battery at current SOC (sets backup reserve)	Grid is cheaper than battery round-trip
SELF_CONSUMPTION	Battery operates naturally	Solar hours, moderate prices

Features

Feature	Description
48-Hour Optimization	Plans battery actions for the next 48 hours
5-Minute Resolution	576 optimization intervals for fine-grained control
Solar Integration	Uses Solcast forecast data for solar predictions
Price Integration	Works with Amber, Octopus, Flow Power, AEMO, and TOU tariffs
EV Load Awareness	Incorporates planned EV charging into the load forecast
Daily Cost Tracking	Actual cost (midnight to now) + predicted cost (now to midnight)
Zero Setup	Built-in — no external integrations or HACS repos needed

Enable Smart Optimization

1. Go to Settings > Devices & Services > PowerSync > Configure
2. Select Smart Optimization (Built-in LP) as your optimization provider
3. Set your backup reserve percentage
4. In the mobile app: Controls > toggle Enable on the Smart Optimization card
5. View the schedule by tapping View Full Schedule

Architecture

+-----------------------------------------------------------+
|  Data Sources                                              |
|  - Amber/Octopus/Flow Power/AEMO prices                   |
|  - Solcast solar forecasts                                 |
|  - Historical load estimation                              |
|  - EV charging plan overlay                                |
+-----------------------------------------------------------+
                           |
                           v
+-----------------------------------------------------------+
|  Built-in LP Optimizer (scipy linprog / HiGHS)             |
|  Collects forecasts -> LP solve -> Optimal schedule        |
|  Fallback: Greedy algorithm if scipy unavailable           |
+-----------------------------------------------------------+
                           |
                           v
+-----------------------------------------------------------+
|  Execution Layer                                           |
|  Schedule -> Battery commands                              |
|  - Tesla: TOU tariff trick                                 |
|  - FoxESS: Work mode + remote control registers            |
|  - Sigenergy/Sungrow: Modbus commands                      |
+-----------------------------------------------------------+

Forecast Sensors

PowerSync creates forecast sensors for dashboard visibility:

Sensor	Description	Unit
sensor.powersync_price_import_forecast	Grid import price forecast	$/kWh
sensor.powersync_price_export_forecast	Feed-in/export price forecast	$/kWh
sensor.powersync_solar_forecast	Solar PV generation forecast	W
sensor.powersync_load_forecast	Home consumption forecast	W

Each sensor includes a forecast attribute with up to 576 data points (48 hours at 5-minute intervals).

Understanding the Schedule

The optimization screen in the mobile app shows:

Section	Description
Status	Whether optimization is active and the current mode
Current/Next Action	What the battery is doing now and what's coming next
Predicted Cost	Estimated electricity cost for the day
Savings	How much you're saving vs no optimization
48-Hour Chart	Visual timeline of SOC and power
Upcoming Actions	List of scheduled charge/discharge periods