Basic system — 5 kWp PV, two deferrable loads#

Type: Tutorial — learning-oriented, follow step by step.

This scenario adds a 5 kWp PV installation to the previous tutorial. No battery yet. We run two optimization modes against this system: a 7-day historical perfect optimization (to see what the optimal schedule would have been with hindsight) and a day-ahead optimization (the real production case).

System#

Component

Value

PV

5 kWp

Battery

none

Deferrable load 1

water heater, 3000 W

Deferrable load 2

pool pump, 750 W

Optimization modes

perfect-optim (backtest), dayahead-optim

Cost function

profit

To enable PV in EMHASS, set set_use_pv: true (default is false) and configure your PV plant via solar_forecast_kwp (for the solar.forecast method) or one of the other weather_forecast_method options. The two deferrable loads use the default nominal_power_of_deferrable_loads: [3000.0, 750.0] from config_defaults.json.

Perfect optimization (7-day historical backtest)#

The perfect-optim mode uses real measured PV production and load data from the last 7 days, so the optimizer has perfect knowledge of inputs. The result is the theoretical best-case cost, useful as a benchmark for what dayahead-optim is approaching.

Run it:

curl -i -H "Content-Type: application/json" \
     -X POST -d '{}' \
     http://localhost:5000/action/perfect-optim

Or the Perfect optimization button in the EMHASS web UI.

Inputs (real measured powers over 7 days):

inputs_power

Load cost and PV selling price:

inputs_cost_price

Result:

optim_results_PV_defLoads_perfectOptim

Cost function over the 7-day period: −26.23 EUR.

Day-ahead optimization#

The dayahead-optim mode is the real production case: forecasted PV (from open-meteo by default; alternatives via weather_forecast_method are solcast, solar.forecast, or the scrapper clearoutside method), forecasted load (1-day persistence by default), forecasted prices (provided at runtime if dynamic).

Run it:

curl -i -H "Content-Type: application/json" \
     -X POST -d '{}' \
     http://localhost:5000/action/dayahead-optim

Result:

optim_results_PV_defLoads_dayaheadOptim

Cost function: −1.56 EUR for the next day. With costfun: profit, this is net cash flow over the period (positive = revenue, negative = expenditure); a less-negative value means lower net cost. Compared with the −5.38 EUR of the no-PV case (see Basic — no PV), the PV installation reduces the daily net spend by about 71%.

Interpretation#

  • perfect-optim (−26.23 EUR over 7 days, ≈ −3.75 EUR/day) gives the theoretical best — the gap to dayahead-optim (−1.56 EUR/day) represents forecast uncertainty.

  • The closer your PV-forecast and load-forecast are to reality, the more dayahead-optim approaches perfect-optim. Forecast quality is the dominant factor — see Good Practices for details.

  • Without a battery, all PV produced beyond instantaneous load is fed to the grid (or curtailed if prod_price ≤ 0). Adding a battery typically improves cost further — see the next tutorial.

See also#