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energy_assistant.yaml.dist
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mqtt:
host: mqtt.example.net
username: x
password: y
topic: energyassistant
home:
name: "My Home"
solar_power: sensor.solaredge_i1_ac_power
solar_energy: sensor.solaredge_i1_ac_energy_kwh
grid_supply_power: sensor.solaredge_m1_ac_power
grid_inverted: False
imported_energy: sensor.solaredge_m1_imported_kwh
exported_energy: sensor.solaredge_m1_exported_kwh
devices:
- name: Evcc loadpoint 1
id: 7d480adc-2c45-4de9-8f36-063c5dea0253
type: evcc
evcc_topic: "evcc"
load_point_id: 1
store_sessions: true
- name: Tumbler
id: 75203c88-216f-4712-8a94-80513793f7e1
type: homeassistant
power: sensor.tumbler_power
energy: sensor.tumbler_energy
energy_scale: 0.001
store_sessions: true
manufacturer: v-zug
model: Adora TS WP
output: switch.tumbler
nominal_power: 800
nominal_duration: 7200
constant: true
- name: Server
id: eb6b3f0a-1175-4ff3-9ebe-8c22663cba48
type: homeassistant
icon: mdi-server-network
power: sensor.server_power
energy: sensor.server_energy
energy_scale: 0.001
emhass:
costfun: profit
hass_entity_prefix: em
retrieve_hass_conf:
historic_days_to_retrieve: 30 # The time step to resample retrieved data from hass in minutes
historic_days_to_retrieve: 8 # We will retrieve data from now and up to days_to_retrieve days
load_negative: False # Set to True if the retrieved load variable is negative by convention
set_zero_min: True # A special treatment for a minimum value saturation to zero. Values below zero are replaced by nans
method_ts_round: "nearest" # Set the method for timestamp rounding, options are: first, last and nearest
optim_conf:
set_use_battery: False # consider a battery storage
delta_forecast_daily: 1 # days
weather_forecast_method: 'scrapper' # options are 'scrapper', 'csv', 'list', 'solcast' and 'solar.forecast'
load_forecast_method: 'naive' # options are 'csv' to load a custom load forecast from a CSV file or 'naive' for a persistence model
load_cost_forecast_method: 'hp_hc_periods' # options are 'hp_hc_periods' for peak and non-peak hours contracts and 'csv' to load custom cost from CSV file
load_peak_hour_periods: # list of different tariff periods (only needed if load_cost_forecast_method='hp_hc_periods')
period_hp_1:
- start: '02:54'
- end: '15:24'
period_hp_2:
- start: '17:24'
- end: '20:24'
load_peak_hours_cost: 0.1907 # peak hours load cost in €/kWh (only needed if load_cost_forecast_method='hp_hc_periods')
load_offpeak_hours_cost: 0.1419 # non-peak hours load cost in €/kWh (only needed if load_cost_forecast_method='hp_hc_periods')
production_price_forecast_method: 'constant' # options are 'constant' for constant fixed value or 'csv' to load custom price forecast from a CSV file
photovoltaic_production_sell_price: 0.065 # power production selling price in €/kWh (only needed if prod_price_forecast_method='constant')
set_total_pv_sell: False # consider that all PV power is injected to the grid (self-consumption with total sell)
lp_solver: 'default' # set the name of the linear programming solver that will be used
lp_solver_path: 'empty' # set the path to the LP solver
set_nocharge_from_grid: False # avoid battery charging from the grid
set_nodischarge_to_grid: True # avoid battery discharging to the grid
set_battery_dynamic: False # add a constraint to limit the dynamic of the battery power in power per time unit
battery_dynamic_max: 0.9 # maximum dynamic positive power variation in percentage of battery maximum power
battery_dynamic_min: -0.9 # minimum dynamic negative power variation in percentage of battery maximum power
weight_battery_discharge: 0.0 # weight applied in cost function to battery usage for discharge
weight_battery_charge: 0.0 # weight applied in cost function to battery usage for charge
plant_conf:
maximum_power_from_grid: 9000 # The maximum power that can be supplied by the utility grid in Watts
maximum_power_to_grid: 9000 # The maximum power that can be supplied to the utility grid in Watts
pv_module_model: # The PV module model
- 'CSUN_Eurasia_Energy_Systems_Industry_and_Trade_CSUN295_60M'
pv_inverter_model: # The PV inverter model
- 'Fronius_International_GmbH__Fronius_Primo_5_0_1_208_240__240V_'
surface_tilt: # The tilt angle of your solar panels
- 30
surface_azimuth: # The azimuth angle of your PV installation
- 205
modules_per_string: # The number of modules per string
- 16
strings_per_inverter: # The number of used strings per inverter
- 1
battery_discharge_power_max: 1000 # If your system has a battery (set_use_battery=True), the maximum discharge power in Watts
battery_charge_power_max: 1000 # If your system has a battery (set_use_battery=True), the maximum charge power in Watts
battery_discharge_efficiency: 0.95 # If your system has a battery (set_use_battery=True), the discharge efficiency
battery_charge_efficiency: 0.95 # If your system has a battery (set_use_battery=True), the charge efficiency
battery_nominal_energy_capacity: 5000 # If your system has a battery (set_use_battery=True), the total capacity of the battery stack in Wh
battery_minimum_state_of_charge: 0.3 # If your system has a battery (set_use_battery=True), the minimum allowable battery state of charge
battery_maximum_state_of_charge: 0.9 # If your system has a battery (set_use_battery=True), the minimum allowable battery state of charge
battery_target_state_of_charge: 0.6 # If your system has a battery (set_use_battery=True), the desired battery state of charge at the end of each optimization cycle