Energy Management for Home Assistant (EMHASS)

Introduction

EMHASS is a powerful energy management and optimization tool designed for residential households connected to Home Assistant (HA).

EMHASS uses a Linear Programming approach to optimize energy usage while considering factors such as electricity prices, power generation from solar panels and energy storage from batteries.

With EMHASS, households can define their energy management objectives and constraints, such as maximizing self-consumption and/or minimizing energy costs, and EMHASS will generate an optimization plan accordingly.

Home Assistant provides the platform for automation.
Custom HA components (e.g. Solcast, EPEXSpot, Sun2, DWD Weather, ...) integrate forecast data into HA.
Huawei Solar Integration exposes multiple services, allowing you to actively control the amount of electricity exported to the grid and forcibly charge/discharge your battery.
Huawei Solar HEMS (designed for the usage of Huawei Solar Integration) assist you with a set of custom HA template sensors, automations and dashboards based on the
EMHASS Optimization plan,

allowing households to create a fully customized and optimized energy management solution.

Overall, the integration of reliable forecast data, Huawei Solar HEMS, EMHASS and Home Assistant offers a comprehensive energy management solution including a machine learning forecaster, a machine learning regressor and an deferrable load thermal model that provides significant cost savings, increased energy efficiency, and greater sustainability for households. By leveraging advanced energy management features and automation capabilities, households can achieve their energy management objectives while enjoying the benefits of more efficient and sustainable energy usage, including optimized EV charging schedules.

Forecast data

Huawei Solar HEMS assist you

to collect all required forecast data,
to adjust them,
to add the data as a list of values to an EMHASS data dictionary,
to automate EMHASS optimization calls,
to pass the data dictionary and all required optimization constraints

using the EMHASS runtimeparams option.

Used dictionary keys to pass your customized forecast data at runtime:

Input	Output	Forecast data
		_{^{Hourly Weather forecasts by the usage of DWD Weather integration. This custom component adds hourly weather data from Deutscher Wetterdienst ( DWD ) into HA.}}
		_{^{Sunrise, Sunset and Solar Noon forecasts by the usage of ha-sun2 integration. This custom component adds sensors that provide information about various sun related events into HA. Additional info: NOAA Solar Calculator}}
_{^{pv_power_forecast}}		_{^{PV Solar forecasts (in Watts) by the usage of Solcast integration. This custom component integrates the Solcast Hobby PV Forecast API into HA.}}
_{^{load_cost_forecast}}		_{^{EPEXSpot electricity price forecasts (in EUR/kWh) by the usage of EPEXSpot integration. This custom component adds electricity prices from the European Power EXchange ( EPEXSpot ) into HA.}}
	_{^{p_load_forecast}}	_{^{House load forecast (in Watts) by the usage of the EMHASS Add-on.}}
	_{^{soc_batt_forecast}}	_{^{Batteries Status Of Charge (SOC) forecast (in %) by the usage of the EMHASS Add-on.}}
	_{^{p_grid_forecast}}	_{^{Grid Import/Export forecast (in Watts) by the usage of the EMHASS Add-on. Forecasted net power flow between your home and the grid. A positive value indicates net export, while a negative value indicates net import.}}

For a complete list of EMHASS computed variables and published data please look at EMHASS documentation.

Optimization plan

The prediction horizon used by Huawei Solar HEMS is following the sun by the usage of Sun2 integration, taking into account your specific location parameters.

Check points

Key	Default	Description
_^Latitude	_{^{HA location parameters}}	_{^{The location's latitude (in degrees).}}
_^Longitude	_{^{HA location parameters}}	_{^{The location's longitude (in degrees).}}
_{^{Observer elevation}}	_^10m	_{^{Observer's elevation above ground level (not sea level) in meters. What affects sunrise & sunset as defined here.}}
_{^{Time zone}}	_{^{HA location parameters}}	_{^{The location's time zone (including Daylight Saving Time (DST). (See the "TZ database name" column at http://en.wikipedia.org/wiki/List_of_tz_database_time_zones.)}}
_^Season		_{^{What affects sunrise & sunset. Additional info: NOAA Solar Calculator}}
_^Midnight		_{^{Midnight local time. Start of new optimization cycle.}}
_^Daylight		_{^{The amount of time between sunrise and sunset (in hours).}}
_{^{Sunrise today}}		_{^{The time in the morning when the sun is 0.833 degrees below the horizon. This is to account for refraction.}}
_{^{PV excess start time today}}	_^15%	_{^{The time at 15% rising offset. = sunrise + daylight * 0.15 Start of Batteries charging under normal conditions.}}
_{^{Solar noon today}}		_{^{The time when the sun is at its highest point.}}
_{^{Prediction end time today}}	_^20%	_{^{The time at 20% setting offset. = sunset - daylight * 0.20 Batteries SOC target: 100%}}
_{^{PV excess end time today}}	_^15%	_{^{The time at 15% setting offset. = sunset - daylight * 0.15 Start of Batteries discharging under normal conditions.}}
_{^{Sunset today}}		_{^{The time in the evening when the sun is 0.833 degrees below the horizon. This is to account for refraction.}}
_{^{Prediction end time tomorrow}}	_^20%	_{^{The time at 20% setting offset. = sunset - daylight * 0.20 End of actual optimization cycle. Batteries SOC target: 100%}}

sensor.emhass_prediction_horizon: [96 - 1]

state: 43
prediction_end_time_today: 2024-10-31 15:10:44.841695+01:00
prediction_end_time_tomorrow: 2024-11-01 15:09:39.014056+01:00
prediction_time_step: 30

Dependencies

Hourly EPEXSpot electricity price forecasts for the next day are normally availiable every day after ~13:00 CET.

To enable an extended optimization cycle, the current day's EPEXSpot forecasts will also be used for the next day until the next day's data is officially provided. At first glance, this approach looks risky, but in reality, this quick and dirty workaround works well.

Solcast integration produce three solar PV generation estimates, broken down by 30 minute and hourly invervals across the day, available for seven days:

central or 50% or most likely to occur PV forecast (or the forecast)
10% or 1 in 10 'worst case' PV forecast assuming more cloud coverage (forecast10)
90% or 1 in 10 'best case' PV forecast assuming less cloud coverage (forecast90)

Definition of the main data entering EMHASS Add-on:

Use sensor.inverter_input_power (Huawei Solar Integration) as the photovoltaic produced power sensor (sensor_power_photovoltaics).
_{^{Note: 7 days of historical data must be available within HA!}}
Use sensor.house_consumption_power (Huawei Solar HEMS) as the household power consumption sensor without any substraction of deferrable loads (sensor_power_load_no_var_loads).
_{^{Note: 7 days of historical data must be available within HA!}}
Use sensor.batteries_state_of_capacity (Huawei Solar Integration) as the sensor for the current Battieries SOC used by the optimization plan.
Use sensor.epex_spot_data_net_price (EPEXSpot integration) as the sensor for the current EPEXSpot price used by the optimization plan.
Use sensor.emhass_forecast_data (Huawei Solar HEMS) as the sensor for pv_power_forecast (attribute='p_pv_forecast') and load_cost_forecast (attribute='epexspot_price_eur_per_kwh') used by the optimization plan.
Use sensor.emhass_prediction_horizon (Huawei Solar HEMS) to calculate all required input for the decreasing prediction horizon used by the optimization plan.

Setup

Use Solcast's central estimate.
Use EPEXSpot and Solcast forecasts for today and tomorrow.
Use 30 minutes Optimization Time Step.
Use NO Deferrable Load.
Use Machine Learning forecaster to forecast the house load power (a basic model fit without training),
- with at least 7 days of historical house load data (but not more then 14 days).
Perform a Model Predictive Control (MPC) optimization by HA automation
- every minute with Prediction end time tomorrow and related decreasing prediction horizon,
- for 2 days of forecasted data delta_forecast_daily,
- with mlforecaster as load forecast method,
- with current BAT SOC as BAT SOC init,
- with 100% BAT SOC target at the end of the optimization plan.

Automation

automation:
  alias: EMHASS - naive mpc optim forecast
  description: EMHASS - naive mpc optimization - upto 48 hours forecast.
  trigger:
    - platform: time_pattern
      minutes: /1
      enabled: true
  action:
    - metadata: {}
      data: {}
      enabled: true
      continue_on_error: false
      response_variable: rest_response
      action: rest_command.emhass_naive_mpc_optim_forecast
    - if:
        - condition: template
          value_template: "{{ rest_response['status'] < 400  }}"
      then:
        - data:
            prefix: all
          enabled: true
          continue_on_error: true
          response_variable: rest_response
          action: rest_command.emhass_publish_data
      else:
        - metadata: {}
          data:
            level: warning
            message: "{{ rest_response['content'] }}"
            logger: EMHASS.MINUTE
          action: system_log.write
  mode: single

rest_command:
  emhass_naive_mpc_optim_forecast:
    url: http://localhost:5000/action/naive-mpc-optim
    method: POST
    content_type: "application/json"
    timeout: 30
    payload: >-
      {% set prediction_horizon = states('sensor.emhass_prediction_horizon')|int(0) %}
      {
        "pv_power_forecast": {{
          ([states('sensor.inverter_input_power')|float(0)] +
          (state_attr('sensor.emhass_forecast_data', 'data')|map(attribute='p_pv_forecast')|list)[1:-1]
          )|tojson
        }},
        "load_cost_forecast": {{
          ([states('sensor.epex_spot_data_net_price')|float(0)|round(2)/100] +
          (state_attr('sensor.emhass_forecast_data', 'data')|map(attribute='epexspot_price_eur_per_kwh')|list)[1:-1] 
          )|tojson 
        }},
        "prediction_horizon": {{prediction_horizon}},
        "soc_init": {{states('sensor.batteries_state_of_capacity')|float(0)/100}},
        "soc_final": 1.0,
        "def_total_hours": [0,0],
        "alpha": 1,
        "beta": 0,
        "continual_publish":false
      }

Logs

Accessible under:

Settings -> Add-ons -> EMHASS -> Log
Settings -> System -> Logs -> EMHASS

2024-09-18 11:29:00,087 - web_server - INFO - Passed runtime parameters: {'pv_power_forecast': [1733.0, 1731, 3689, 5519, 5712, 5341, 4897, 4351, 3747, 3057, 2269, 1477, 762, 349, 275, 185, 86, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9, 297, 1159, 2020, 2741, 3394, 4036, 4662, 5276, 5742, 5905, 5921, 5676, 5321, 4848, 4327, 3678, 2988, 2209, 1451, 778, 396, 295, 186, 76, 0, 0, 0, 0, 0, 0, 0, 0], 'load_cost_forecast': [0.26539999999999997, 0.26544, 0.20614, 0.20614, 0.19961, 0.19961, 0.1995, 0.1995, 0.20085, 0.20085, 0.24727, 0.24727, 0.30376, 0.30376, 0.33516, 0.33516, 0.35422, 0.35422, 0.32837, 0.32837, 0.31058, 0.31058, 0.30662, 0.30662, 0.2942, 0.2942, 0.30095, 0.30095, 0.29593, 0.29593, 0.29923, 0.29923, 0.29918, 0.29918, 0.2978, 0.2978, 0.30662, 0.30662, 0.32467, 0.32467, 0.3685, 0.3685, 0.35796, 0.35796, 0.32151, 0.32151, 0.29362, 0.29362, 0.26544, 0.26544, 0.20614, 0.20614, 0.19961, 0.19961, 0.1995, 0.1995, 0.20085, 0.20085, 0.24727, 0.24727, 0.30376, 0.30376, 0.33516, 0.33516, 0.35422, 0.35422, 0.32837, 0.32837, 0.31058, 0.31058, 0.30662, 0.30662, 0.2942], 'prediction_horizon': 59, 'soc_init': 0.35, 'soc_final': 1.0, 'def_total_hours': [0, 0], 'alpha': 1, 'beta': 0, 'continual_publish': False}
2024-09-18 11:29:00,087 - web_server - INFO -  >> Setting input data dict
2024-09-18 11:29:00,087 - web_server - INFO - Setting up needed data
2024-09-18 11:29:00,089 - web_server - INFO - Retrieve hass get data method initiated...
2024-09-18 11:29:05,927 - web_server - INFO - Retrieving weather forecast data using method = list
2024-09-18 11:29:05,927 - web_server - INFO - Retrieving data from hass for load forecast using method = mlforecaster
2024-09-18 11:29:05,928 - web_server - INFO - Retrieve hass get data method initiated...
2024-09-18 11:29:17,467 - web_server - INFO -  >> Performing naive MPC optimization...
2024-09-18 11:29:17,467 - web_server - INFO - Performing naive MPC optimization
2024-09-18 11:29:17,471 - web_server - INFO - Perform an iteration of a naive MPC controller
2024-09-18 11:29:17,582 - web_server - INFO - Status: Optimal
2024-09-18 11:29:17,582 - web_server - INFO - Total value of the Cost function = 2.05
2024-09-18 11:29:17,709 - web_server - INFO - Passed runtime parameters: {}
2024-09-18 11:29:17,709 - web_server - INFO -  >> Setting input data dict
2024-09-18 11:29:17,709 - web_server - INFO - Setting up needed data
2024-09-18 11:29:17,710 - web_server - INFO -  >> Publishing data...
2024-09-18 11:29:17,710 - web_server - INFO - Publishing data to HASS instance
2024-09-18 11:29:17,718 - web_server - INFO - Successfully posted to sensor.p_pv_forecast = 1733.0
2024-09-18 11:29:17,724 - web_server - INFO - Successfully posted to sensor.p_load_forecast = 899.31
2024-09-18 11:29:17,728 - web_server - INFO - Successfully posted to sensor.p_pv_curtailment = 0.0
2024-09-18 11:29:17,734 - web_server - INFO - Successfully posted to sensor.p_hybrid_inverter = 899.31
2024-09-18 11:29:17,739 - web_server - INFO - Successfully posted to sensor.p_deferrable0 = 0.0
2024-09-18 11:29:17,744 - web_server - INFO - Successfully posted to sensor.p_batt_forecast = -833.69
2024-09-18 11:29:17,750 - web_server - INFO - Successfully posted to sensor.soc_batt_forecast = 37.5
2024-09-18 11:29:17,755 - web_server - INFO - Successfully posted to sensor.p_grid_forecast = 0.0
2024-09-18 11:29:17,760 - web_server - INFO - Successfully posted to sensor.total_cost_fun_value = 2.05
2024-09-18 11:29:17,764 - web_server - INFO - Successfully posted to sensor.optim_status = Optimal
2024-09-18 11:29:17,768 - web_server - INFO - Successfully posted to sensor.unit_load_cost = 0.2654
2024-09-18 11:29:17,773 - web_server - INFO - Successfully posted to sensor.unit_prod_price = 0.08

Configuration parameters

Below are the needed configuration parameters to correctly use the EMHASS-Add-on.
This add-on uses the EMHASS core module.

EMHASS configuration parameters are detailed in the EMHASS documentation.

YAML export of configuration parameters.

Home Assistant (HA) integration

Parameter	Description	Options	Default	Value
_{^hass_url}	_{^{URL of your HA instance.}}		_^empty	_^empty
_{^{long_lived_token}}	_{^{A Long-Lived Access Token that can be created from the Lovelace profile page.}}		_^empty	_^empty
_{^data_path}	_{^{Set folder path where EMHASS data will be stored. Selecting /data/ will store data in an enclosed folder, only assessable by the addon itself. Select /share/ to store data in the mounted share folder, accessible outside of the Addon.}}	_{^{default /data/ /share/}}	_^default	_^default
_{^{logging_level}}	_{^{Set the logger level. Change to WARNING or ERROR to reduce logging if needed.}}	_{^{DEBUG INFO WARNING ERROR}}	_^INFO	_^INFO

_{^{optimization_time_step}}	_{^{The time step (in mins) to resample retrieved data from HA. It should not be defined too low or you will run into memory problems when defining the Linear Programming optimization.}}		_³⁰	_³⁰
_{^{historic_days_to_retrieve}}	_{^{We will retrieve data from now and up to 'historic_days_to_retrieve' days. This will be used for the perfect-optim task.}}		_²	_⁷
_{^{delta_forecast_daily}}	_{^{Number of days for forecasted data (1 day = 1).}}		_¹	_²
_{^{method_ts_round}}	_{^{Method for timestamp rounding.}}	_{^{first last nearest}}	_^nearest	_^first

_{^lp_solver}	_{^{Name of the linear programming solver.}}	_{^{PULP_CBC_CMD GLPK_CMD COIN_CMD}}	_{^COIN_CMD}	_{^COIN_CMD}
_{^{lp_solver_path}}	_{^{Path to the LP solver.}}		_{^/usr/bin/cbc}	_{^/usr/bin/cbc}
_{^{load_forecast_method}}	_{^{Load forecast method. Select 'mlforecaster' to use the machine learning forecaster.}}	_{^{naive csv list mlforecaster}}	_^naive	_{^mlforecaster}

Deferrable Load

Parameter	Description	Options	Default	Value
_{^{number_of_deferrable_loads}}	_{^{Number of deferrable loads to consider.}}		_²	_¹
_{^{nominal_power_of_deferrable_loads}}	_{^{Nominal power for each deferrable load (in Watts). Note: Number of items = Number_of_deferrable_loads}}		_^0 0	_^0 0
_{^{operating_hours_of_each_deferrable_load}}	_{^{Total number of hours that each deferrable load should operate. Note: Number of items = Number_of_deferrable_loads}}		_^0 0	_^0 0
_{^{start_timesteps_of_each_deferrable_load}}	_{^{The timestep as from which each deferrable load is allowed to operate. Operation before this timestep is not allowed. If value 0 is given, the deferrable load will be optimized as from the beginning of the complete prediction horizon window. Note: Number of items = Number_of_deferrable_loads}}		_^0 0	_^0 0
_{^{end_timesteps_of_each_deferrable_load}}	_{^{The timestep before which each deferrable load should operate. Operation after this timestep is not allowed. If value 0 is given, the deferrable load optimization window will extend up to the end of the prediction horizon window. Note: Number of items = Number_of_deferrable_loads}}		_^0 0	_^0 0
_{^{peak_hours_periods_start_hours}}	_{^{Start hours of peak hours periods (in 24h HH:MM format). Note: Number of items = Number_of_deferrable_loads}}		_^0 0	_{^{"05:54" "17:54"}}
_{^{peak_hours_periods_end_hours}}	_{^{End hours of peak hours periods (in 24h HH:MM format). Note: Number of items = Number_of_deferrable_loads}}		_^0 0	_{^{"09:24" "20:24"}}
_{^{treat_deferrable_load_as_semi_cont}}	_{^{Define if we should treat each deferrable load as a semi-continuous variable. Semi-continuous variables are variables that can take either their nominal value or zero. Note: Number of items = Number_of_deferrable_loads}}	_^true false	_^true true	_^true true
_{^{set_deferrable_load_single_constant}}	_{^{Set each deferrable load as a constant fixed value variable with just one startup for each optimization task (ex. each 24h) Note: Number of items = Number_of_deferrable_loads}}	_^true false	_^false false	_^false false
_{^{set_deferrable_startup_penalty}}	_{^{Add a penalty P (Power) each time the deferrable load turns on. The deferrable load turns on will incur an additional cost of P * list_nominal_power_of_deferrable_loads * cost_of_electricity at that time. Note: Number of items = Number_of_deferrable_loads}}		_^0 0	_^0 0

Electricity spot price forecast

Parameter	Description	Options	Default	Value	Entity	Setting
_{^{load_cost_forecast_method}}	_{^{Define the method that will be used for load cost forecast. The options are 'hp_hc_periods' for peak and non-peak hours contracts and 'csv' to load custom cost from CSV file.}}	_{^{hp_hc_periods csv}}	_{^{hp_hc_periods}}	_{^{hp_hc_periods}}	_{^{sensor.emhass_forecast_data}}	_{^{attribute='price_eur_per_kwh'}}
_{^{production_price_forecast_method}}	_{^{Define the method that will be used for PV power production price forecast. This is the price that is payed by the utility for energy injected to the grid. The options are 'constant' for constant fixed value or 'csv' to load custom price forecast from a CSV file.}}	_{^constant csv}	_^constant	_^constant
_{^{load_peak_hours_cost}}	_{^{The cost of the electrical energy from the grid during peak hours (in €/kWh).}}		_^0.1907	_^0.36
_{^{load_offpeak_hours_cost}}	_{^{The cost of the electrical energy from the grid during non-peak hours (in €/kWh).}}		_^0.1419	_^0.28
_{^{photovoltaic_production_sell_price}}	_{^{The paid price for energy injected to the grid from excedent PV production (in €/kWh).}}		_^0.065	_^0.08

PV power production forecast

Although Huawei Solar HEMS uses the HA Solcast integration for PV power production forecasts, all parameters below must be properly configured because for some modeling task EMHASS relies on the PVLib Python package.

EMHASS PV lib database - dedicated web app is useful for finding your inverter and solar panel models used by the PVLib module.

More info at:

Attention: The knowledge about below technical parameters is crusial for reliable forcasting whatever tool or service you will use!

Parameter	Description	Options	Default	Value	Entity	Setting
_{^{weather_forecast_method}}	_{^{Weather forecast method.}}	_{^{scrapper (ClearOutside) Solcast solar.forecast csv}}	_^scrapper	_^scrapper	_{^{sensor.emhass_forecast_data}}	_{^{attribute='p_pv_forecast'}}
_{^{pv_module_model}}	_{^{PV module model. Note: This parameter can be a list of strings to enable the simulation of mixed orientation systems, for example one east-facing array (azimuth=90) and one west-facing array (azimuth=270).}}			_{^{Trina_Solar_TSM_390DE09_05}}
_{^{pv_inverter_model}}	_{^{PV inverter model. Note: This parameter can be a list of strings to enable the simulation of mixed orientation systems, for example one east-facing array (azimuth=90) and one west-facing array (azimuth=270).}}			_{^{SolaX_Power_Network_Technology__ Zhe_jiang__Co___Ltd___A1_Hybrid_7_0_US__240V_}}
_{^surface_tilt}	_{^{Tilt angle of your solar panels. Note: This parameter can be a list of integers to enable the simulation of mixed orientation systems, for example one east-facing array (azimuth=90) and one west-facing array (azimuth=270).}}	_{^{[0 - 90]}}	_³⁰	_⁴⁵
_{^{surface_azimuth}}	_{^{Azimuth of your PV installation. Note: This parameter can be a list of integers to enable the simulation of mixed orientation systems, for example one east-facing array (azimuth=90) and one west-facing array (azimuth=270).}}	_{^{[0 - 360]}}	_²⁰⁵	_¹³⁹
_{^{modules_per_string}}	_{^{Number of modules per string. Note: This parameter can be a list of integers to enable the simulation of mixed orientation systems, for example one east-facing array (azimuth=90) and one west-facing array (azimuth=270).}}		_¹⁶	_¹⁰
_{^{strings_per_inverter}}	_{^{Number of used strings per inverter. Note: This parameter can be a list of integers to enable the simulation of mixed orientation systems, for example one east-facing array (azimuth=90) and one west-facing array (azimuth=270).}}		_¹	_²

Huawei Smart Energy Controller

Parameter	Description	Options	Default	Value	Entity
_{^{sensor_power_photovoltaics}}	_{^{Name of the photovoltaic produced power sensor from Home Assistant (in Watts).}}			_{^{sensor.inverter_input_power}}	_{^{sensor.inverter_input_power}}
_{^{sensor_power_load_no_var_loads}}	_{^{Name of the household power consumption sensor from Home Assistant (in Watts). The deferrable loads that we will want to include in the optimization problem should be substracted from this sensor in HASS.}}			_{^{sensor.house_consumption_power}}	_{^{sensor.house_consumption_power}}
_{^{load_negative}}	_{^{If the retrived load variable is negative by convention}}	_^true false	_^false	_^false	_{^{sensor.house_consumption_power}}
_{^set_zero_min}	_{^{To give a special treatment for a minimum value saturation to zero for power consumption data. Values below zero are replaced by nans.}}	_^true false	_^true	_^true	_{^{sensor.house_consumption_power}}
_{^{maximum_power_from_grid}}	_{^{The maximum power that can be supplied by the utility grid (consumption in Watts).}}		_⁹⁰⁰⁰	_⁹⁰⁰⁰
_{^{maximum_power_to_grid}}	_{^{The maximum power that can be supplied to the utility grid (injection in Watts).}}		_⁹⁰⁰⁰	_⁶⁶⁰⁰	_{^{sensor.inverter_power}}
_{^{inverter_is_hybrid}}	_{^{Set to True to consider that the inverter is hybrid for PV and batteries. A special parameter to consider that the inverter is hybrid for PV and batteries.}}	_^true false	_^false	_^true
_{^{compute_curtailment}}	_{^{Set to True to compute a PV curtailment variable. A special parameter to define if we will compute a PV curtailment variable.}}	_^true false	_^false	_^true

Huawei Smart String ESS

Parameter	Description	Options	Default	Value	Entity	Setting
_{^{set_use_battery}}	_{^{Battery is present. Set to True if we should consider an energy storage device such as a Li-Ion battery.}}	_^true false	_^false	_^true
_^costfun	_{^{Type of cost function (optional).}}	_{^{profit cost self-consumption}}	_^profit	_{^{self-consumption}}	_{^{select.batteries_working_mode}}	_{^{Maximise self consumption}}
_{^{set_total_pv_sell}}	_{^{Set this parameter to true to consider that all the PV power produced is injected to the grid (No direct self-consumption). The default is false, for a system with direct self-consumption.}}	_^true false	_^false	_^false	_{^{select.batteries_working_mode}}	_{^{Maximise self consumption}}
_{^{set_nocharge_from_grid}}	_{^{Set this to true if you want to forbidden to charge the battery from the grid. The battery will only be charged from excess PV.}}	_^true false	_^false	_^false	_{^{switch.batteries_charge_from_grid}}	_{^{on/off by HA automation}}
_{^{set_nodischarge_to_grid}}	_{^{Set this to true if you want to forbidden to discharge the battery power to the grid.}}	_^true false	_^false	_^true	_{^{select.batteries_excess_pv_energy_use_in_tou}}	_^Charge
_{^{set_battery_dynamic}}	_{^{Set a power dynamic limiting condition to the battery power. This is an additional constraint on the battery dynamic in power per unit of time.}}	_^true false	_^false	_^false
_{^{battery_dynamic_max}}	_{^{The maximum positive battery power dynamic. This is the power variation in percentage of battery maximum power.}}		_^0.9	_¹
_{^{battery_dynamic_min}}	_{^{The minimum negative battery power dynamic. This is the power variation in percentage of battery maximum power.}}		_^-0.9	_^-1
_{^{weight_battery_discharge}}	_{^{An additional weight applied in cost function to battery usage for discharge (currency/kWh).}}		_^0.0	_^0.0
_{^{weight_battery_charge}}	_{^{An additional weight applied in cost function to battery usage for charge (currency/kWh).}}		_^0.0	_^0.0
_{^{battery_discharge_power_max}}	_{^{Maximum discharge power (in Watts).}}		_¹⁰⁰⁰	_⁶⁶⁰⁰	_{^{sensor.batteries_maximum_discharge_power}}
_{^{battery_charge_power_max}}	_{^{Maximum charge power (in Watts).}}		_¹⁰⁰⁰	_⁷⁵⁰⁰	_{^{sensor.batteries_maximum_charge_power}}
_{^{battery_discharge_efficiency}}	_{^{Discharge efficiency.}}		_^0.95	_^0.9	???
_{^{battery_charge_efficiency}}	_{^{Charge efficiency.}}		_^0.95	_^0.9	???
_{^{battery_nominal_energy_capacity}}	_{^{Total capacity of the battery stack (in Wh).}}		_⁵⁰⁰⁰	_{¹⁵⁰⁰⁰}	_{^{sensor.batteries_rated_capacity}}
_{^{battery_minimum_state_of_charge}}	_{^{Minimum allowable battery state of charge.}}		_^0.3.	_^0.05	_{^{number.batteries_end_of_discharge_soc}}	_^5%
_{^{battery_maximum_state_of_charge}}	_{^{Maximum allowable battery state of charge.}}		_^0.9.	_^1.0	_{^{number.batteries_end_of_charge_soc}}	_^100%
_{^{battery_target_state_of_charge}}	_{^{Desired battery state of charge at the end of each optimization cycle.}}		_^0.6.	_^1.0	_{^{number.batteries_end_of_charge_soc}}	_^100%

EMHASS Add‐on - heinemannj/huawei_solar_hems GitHub Wiki

Energy Management for Home Assistant (EMHASS)

Introduction

Forecast data

Optimization plan

Check points

Dependencies

Setup

Automation

Logs

Configuration parameters

Home Assistant (HA) integration

Deferrable Load

Electricity spot price forecast

PV power production forecast

Huawei Smart Energy Controller

Huawei Smart String ESS

Help to improve the documentation

⚠️ GitHub.com Fallback ⚠️

EMHASS Add‐on - heinemannj/huawei_solar_hems GitHub Wiki

Energy Management for Home Assistant (EMHASS)

Introduction

Forecast data

Optimization plan

Check points

Dependencies

Setup

Automation

Logs

Configuration parameters

Home Assistant (HA) integration

Deferrable Load

Electricity spot price forecast

PV power production forecast

Huawei Smart Energy Controller

Huawei Smart String ESS

Help to improve the documentation

⚠️ **GitHub.com Fallback** ⚠️

⚠️ GitHub.com Fallback ⚠️