Please check the format of your - peak_hours_xxx_hours:
The format should be “06:24” instead of 624.
I ran into the same problem on my initial install of EMHASS.
I know! This is the default config. You will get this problem, when you restore to default.
it would be great if a developer can fix this.
Has anyone tried or is it possible to model duty cycle constraints? For example:
- When my dishwasher runs it will run for 2 consecutive hours. It’s no use for EMHASS to schedule it to run for two non-consecutive hours.
- When my hot water heater runs it heats the tank fully in 2 hours. There is no point in scheduling it for 6 consecutive hours in the morning (I will work around this with MPC but it would be better if it were taken into account originally).
- In general, equipment may have duty cycle constraints for longevity (e.g. max 2 h on, min 1 h off).
This issue is a mystery! I don’t know how to solve it. There is an issue on github that was closed because a solution was proposed but it is still failing. I will reopen it. Help is wanted.
You can use the control variable set_def_constant to achieve this behavior with your dishwater. Set it to true for the dishwasher and set the 2h operating time.
For your example of the water heater it can be achieved by splitting your deferrable load into several variables, for example water-heater-day and water-heater-night with operating times as you wish.
2 Likes
Hi guys,
I am still struggling to make the add-on work. Its seems that I get no data from home assistant.
It is problably a noob mistake, but I couldnt find anything in the manual or in this forum about it.
Has anyone a hint for me?
Best
T
2024-01-30 09:26:49,271 - web_server - ERROR - Variable sensor.growatt_total_enegry_usage_actual_2 was not found. This is typically because no data could be retrieved from Home Assistant
2024-01-30 09:26:49,277 - web_server - ERROR - Exception on /action/dayahead-optim [POST]
Please share your configuration to see if there is anything wrong. Typically people have these errors when setting the data fetch URL
Sure, it is pretty standard. Havent changed much yet:
costfun: self-consumption
logging_level: INFO
set_total_pv_sell: false
set_nocharge_from_grid: true
set_nodischarge_to_grid: true
sensor_power_photovoltaics: sensor.growatt_pv_energy_calculated_total_actual
sensor_power_load_no_var_loads: sensor.growatt_total_enegry_usage_actual_2
number_of_deferrable_loads: 2
list_nominal_power_of_deferrable_loads:
- nominal_power_of_deferrable_loads: 3600
list_operating_hours_of_each_deferrable_load:
- operating_hours_of_each_deferrable_load: 8
list_start_timesteps_of_each_deferrable_load:
- start_timesteps_of_each_deferrable_load: 0
- start_timesteps_of_each_deferrable_load: 0
list_end_timesteps_of_each_deferrable_load:
- end_timesteps_of_each_deferrable_load: 0
- end_timesteps_of_each_deferrable_load: 0
list_peak_hours_periods_start_hours:
- peak_hours_periods_start_hours: "05:54"
- peak_hours_periods_start_hours: "17:54"
list_peak_hours_periods_end_hours:
- peak_hours_periods_end_hours: "09:24"
- peak_hours_periods_end_hours: "21:24"
list_treat_deferrable_load_as_semi_cont:
- treat_deferrable_load_as_semi_cont: true
- treat_deferrable_load_as_semi_cont: true
list_set_deferrable_load_single_constant:
- set_deferrable_load_single_constant: false
- set_deferrable_load_single_constant: false
load_peak_hours_cost: 0.3507
load_offpeak_hours_cost: 0.2519
photovoltaic_production_sell_price: 0.093
maximum_power_from_grid: 22000
list_pv_module_model:
- pv_module_model: CSUN_Eurasia_Energy_Systems_Industry_and_Trade_CSUN295_60M
list_pv_inverter_model:
- pv_inverter_model: Fronius_International_GmbH__Fronius_Primo_5_0_1_208_240__240V_
list_surface_tilt:
- surface_tilt: 30
list_surface_azimuth:
- surface_azimuth: 205
list_modules_per_string:
- modules_per_string: 6
list_strings_per_inverter:
- strings_per_inverter: 2
set_use_battery: true
battery_nominal_energy_capacity: 6500
hass_url: http://192.168.1.134:8123
long_lived_token: >-
WASCHANGENDUPUPONPOSTINGSONOTWORTHTRYINGCI6IkpXVCJ9.eyJpc3MiOiIxZWNkOGMyYTNhNzA0Y2E1YWNiNjhkNDVlNTBiNmU1OSIsImlhdCI6MTcwNjYwMjg3OSwiZXhwIjoyMDIxOTYyODc5fQ._yldHu_yWEIBSIMzpugDsX-OSGNKcP6fOaPB4saaGCU
optimization_time_step: 30
historic_days_to_retrieve: 2
method_ts_round: nearest
lp_solver: COIN_CMD
lp_solver_path: /usr/bin/cbc
set_battery_dynamic: false
battery_dynamic_max: 0.9
battery_dynamic_min: -0.9
weight_battery_discharge: 1
weight_battery_charge: 1
load_forecast_method: naive
battery_discharge_power_max: 3650
battery_charge_power_max: 3650
battery_discharge_efficiency: 0.95
battery_charge_efficiency: 0.95
battery_minimum_state_of_charge: 0.3
battery_maximum_state_of_charge: 0.9
battery_target_state_of_charge: 0.6
This is the problem.
Set these to:
hass_url: empty
long_lived_token: empty
You would only need to set these if you are using the docker standalone mode. These are not needed with the add-on on HA OS.
Thanks, brings me to s different error 
2024-01-30 11:57:20,839 - web_server - INFO - Setting up needed data
2024-01-30 11:57:20,846 - web_server - INFO - Retrieving weather forecast data using method = scrapper
2024-01-30 11:57:25,495 - web_server - INFO - Retrieving data from hass for load forecast using method = mlforecaster
2024-01-30 11:57:25,522 - web_server - INFO - Retrieve hass get data method initiated...
2024-01-30 11:57:33,088 - web_server - ERROR - The ML forecaster file was not found, please run a model fit method before this predict method
2024-01-30 11:57:33,091 - web_server - ERROR - Exception on /action/dayahead-optim [POST]
Traceback (most recent call last):
File "/usr/local/lib/python3.11/dist-packages/flask/app.py", line 1463, in wsgi_app
response = self.full_dispatch_request()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/flask/app.py", line 872, in full_dispatch_request
rv = self.handle_user_exception(e)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/flask/app.py", line 870, in full_dispatch_request
rv = self.dispatch_request()
^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/flask/app.py", line 855, in dispatch_request
return self.ensure_sync(self.view_functions[rule.endpoint])(**view_args) # type: ignore[no-any-return]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/emhass/web_server.py", line 50, in action_call
input_data_dict = set_input_data_dict(config_path, str(data_path), costfun,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/emhass/command_line.py", line 91, in set_input_data_dict
P_load_forecast = fcst.get_load_forecast(method=optim_conf['load_forecast_method'])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/emhass/forecast.py", line 616, in get_load_forecast
forecast_out = mlf.predict(data_last_window)
^^^^^^^^^^^
AttributeError: 'NoneType' object has no attribute 'predict'
Before running with mlforecaster you must train the model with fit.
Thanks, but tried it also differently:
2024-01-30 13:22:16,343 - web_server - WARNING - The data container dictionary is empty... Please launch an optimization task
2024-01-30 13:22:40,193 - web_server - INFO - Setting up needed data
2024-01-30 13:22:40,395 - web_server - INFO - Retrieve hass get data method initiated...
2024-01-30 13:22:47,940 - web_server - ERROR - Exception on /action/perfect-optim [POST]
Traceback (most recent call last):
``OK, thank you guys. Started working after the last update 
1 Like
My EMHASS plan looks good but when it comes time to publish the plan everything seems to be off by 30 minutes.
I am using optimization_time_step: 60 in the dayahead and MPC calls. Do I need to pass optimization_time_step: 60 to publish-data as well?
If not, where should I start to investigate what is going wrong?
What time do you publish and what rounding are you using?
Publishing every 5 minutes. Not sure what rounding you’re referring to?
Hi,
Anyone else having this error?
Cannot infer dst time from 2024-04-07 02:00:00, try using the ‘ambiguous’ argument
I was using an old version (0.4 I think), so I tried to setup the EMHASS addon but having a similar issue
2024-04-06 14:21:32,598 - web_server - INFO - EMHASS server online, serving index.html…
2024-04-06 14:21:33,297 - web_server - INFO - Passed runtime parameters: {‘prod_price_forecast’: [0.03, 0.02, 0.03, 0.02, 0.02, 0.05, 0.07, 0.08, 0.11, 0.12, 0.13, 0.11, 0.12, 0.12, 0.11, 0.11, 0.11, 0.11, 0.14, 0.12, 0.11, 0.14, 0.13, 0.11, 0.11, 0.11, 0.11, 0.09, 0.09, 0.08, 0.08, 0.06, 0.02, 0.01, 0.06, 0.07, 0.06, 0.05, 0.05, 0.06, 0.05, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], ‘load_cost_forecast’: [0.14, 0.13, 0.27, 0.27, 0.27, 0.3, 0.33, 0.33, 0.37, 0.37, 0.38, 0.37, 0.37, 0.37, 0.22, 0.22, 0.23, 0.22, 0.26, 0.23, 0.23, 0.26, 0.24, 0.23, 0.23, 0.23, 0.22, 0.2, 0.2, 0.2, 0.19, 0.17, 0.13, 0.12, 0.17, 0.18, 0.17, 0.16, 0.16, 0.16, 0.16, 0.12, 0.12, 0.12, 0.12, 0.12, 0.12, 0.12, 0.12], ‘load_power_forecast’: [754, 1700, 1200, 900, 900, 800, 1400, 1400, 600, 500, 600, 900, 1200, 1300, 1200, 1300, 1300, 1000, 500, 500, 500, 400, 500, 500, 500, 500, 400, 400, 400, 400, 400, 400, 400, 400, 400, 400, 800, 1000, 2000, 1300, 900, 1600, 1500, 1700, 1900, 2000, 1800, 1900], ‘pv_power_forecast’: [2639, 1627, 1618, 1653, 1608, 1409, 1040, 592, 184, 52, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9, 104, 358, 832, 1147, 1396, 1573, 1731, 1896, 1983, 1992, 2052, 2162, 2264, 2352, 2438, 2490, 2292, 1885, 1399, 987, 461, 103, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], ‘prediction_horizon’: 48, ‘alpha’: 1, ‘beta’: 0, ‘num_def_loads’: 0, ‘soc_init’: 0.63, ‘soc_final’: 0.05}
2024-04-06 14:21:33,297 - web_server - INFO - >> Setting input data dict
2024-04-06 14:21:33,297 - web_server - INFO - Setting up needed data
2024-04-06 14:21:33,304 - web_server - ERROR - Exception on /action/naive-mpc-optim [POST]
Traceback (most recent call last):
File “/usr/local/lib/python3.11/dist-packages/flask/app.py”, line 1463, in wsgi_app
response = self.full_dispatch_request()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File “/usr/local/lib/python3.11/dist-packages/flask/app.py”, line 872, in full_dispatch_request
rv = self.handle_user_exception(e)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File “/usr/local/lib/python3.11/dist-packages/flask/app.py”, line 870, in full_dispatch_request
rv = self.dispatch_request()
^^^^^^^^^^^^^^^^^^^^^^^
File “/usr/local/lib/python3.11/dist-packages/flask/app.py”, line 855, in dispatch_request
return self.ensure_sync(self.view_functions[rule.endpoint])(**view_args) # type: ignore[no-any-return]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File “/usr/local/lib/python3.11/dist-packages/emhass/web_server.py”, line 108, in action_call
input_data_dict = set_input_data_dict(config_path, str(data_path), costfun,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File “/usr/local/lib/python3.11/dist-packages/emhass/command_line.py”, line 64, in set_input_data_dict
fcst = Forecast(retrieve_hass_conf, optim_conf, plant_conf,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File “/usr/local/lib/python3.11/dist-packages/emhass/forecast.py”, line 164, in init
freq=self.freq).round(self.freq, ambiguous=‘infer’, nonexistent=‘shift_forward’)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File “/usr/local/lib/python3.11/dist-packages/pandas/core/indexes/extension.py”, line 98, in method
result = attr(self._data, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File “/usr/local/lib/python3.11/dist-packages/pandas/core/arrays/datetimelike.py”, line 2026, in round
return self._round(freq, RoundTo.NEAREST_HALF_EVEN, ambiguous, nonexistent)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File “/usr/local/lib/python3.11/dist-packages/pandas/core/arrays/datetimelike.py”, line 2002, in _round
return result.tz_localize(
^^^^^^^^^^^^^^^^^^^
File “/usr/local/lib/python3.11/dist-packages/pandas/core/arrays/_mixins.py”, line 86, in method
return meth(self, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File “/usr/local/lib/python3.11/dist-packages/pandas/core/arrays/datetimes.py”, line 1040, in tz_localize
new_dates = tzconversion.tz_localize_to_utc(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File “pandas/_libs/tslibs/tzconversion.pyx”, line 322, in pandas._libs.tslibs.tzconversion.tz_localize_to_utc
File “pandas/_libs/tslibs/tzconversion.pyx”, line 637, in pandas._libs.tslibs.tzconversion._get_dst_hours
pytz.exceptions.AmbiguousTimeError: 2024-04-07 02:00:00
2024-04-06 14:21:33,330 - web_server - INFO - Passed runtime parameters: {}
2024-04-06 14:21:33,331 - web_server - INFO - >> Setting input data dict
2024-04-06 14:21:33,331 - web_server - INFO - Setting up needed data
2024-04-06 14:21:33,335 - web_server - ERROR - Exception on /action/publish-data [POST]
Traceback (most recent call last):
EDIT 2: Now seems to be working
2024-04-06 14:32:33,293 - web_server - INFO - Passed runtime parameters: {‘prod_price_forecast’: [0.02, 0.02, 0.02, 0.02, 0.05, 0.07, 0.08, 0.11, 0.12, 0.13, 0.11, 0.12, 0.12, 0.11, 0.11, 0.11, 0.11, 0.14, 0.12, 0.11, 0.14, 0.13, 0.11, 0.11, 0.11, 0.11, 0.09, 0.09, 0.08, 0.08, 0.06, 0.02, 0.01, 0.06, 0.07, 0.06, 0.05, 0.05, 0.06, 0.05, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01], ‘load_cost_forecast’: [0.13, 0.27, 0.27, 0.27, 0.3, 0.33, 0.33, 0.37, 0.37, 0.38, 0.37, 0.37, 0.37, 0.22, 0.22, 0.23, 0.22, 0.26, 0.23, 0.23, 0.26, 0.24, 0.23, 0.23, 0.23, 0.22, 0.2, 0.2, 0.2, 0.19, 0.17, 0.13, 0.12, 0.17, 0.18, 0.17, 0.16, 0.16, 0.16, 0.16, 0.12, 0.12, 0.12, 0.12, 0.12, 0.12, 0.12, 0.12, 0.12], ‘load_power_forecast’: [872, 1200, 900, 900, 800, 1400, 1400, 600, 500, 600, 900, 1200, 1300, 1200, 1300, 1300, 1000, 500, 500, 500, 400, 500, 500, 500, 500, 400, 400, 400, 400, 400, 400, 400, 400, 400, 400, 800, 1000, 2000, 1300, 900, 1600, 1500, 1700, 1900, 2000, 1800, 1900, 1300], ‘pv_power_forecast’: [2954, 1618, 1653, 1608, 1409, 1040, 592, 184, 52, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9, 104, 358, 832, 1147, 1396, 1573, 1731, 1896, 1983, 1992, 2052, 2162, 2264, 2352, 2438, 2490, 2292, 1885, 1399, 987, 461, 103, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], ‘prediction_horizon’: 48, ‘alpha’: 1, ‘beta’: 0, ‘num_def_loads’: 0, ‘soc_init’: 0.66, ‘soc_final’: 0.05}
2024-04-06 14:32:33,295 - web_server - INFO - >> Setting input data dict
2024-04-06 14:32:33,295 - web_server - INFO - Setting up needed data
2024-04-06 14:32:33,297 - web_server - INFO - Retrieve hass get data method initiated…
2024-04-06 14:32:36,342 - web_server - INFO - Retrieving weather forecast data using method = list
2024-04-06 14:32:36,352 - web_server - INFO - >> Performing naive MPC optimization…
2024-04-06 14:32:36,353 - web_server - INFO - Performing naive MPC optimization
2024-04-06 14:32:36,369 - web_server - INFO - Perform an iteration of a naive MPC controller
2024-04-06 14:32:36,409 - web_server - WARNING - Solver default unknown, using default
Welcome to the CBC MILP Solver
Version: 2.10.3
Build Date: Dec 15 2019
command line - /usr/local/lib/python3.11/dist-packages/pulp/solverdir/cbc/linux/64/cbc /tmp/7541742d607049e0967f6fb68c9c42b2-pulp.mps -max -timeMode elapsed -branch -printingOptions all -solution /tmp/7541742d607049e0967f6fb68c9c42b2-pulp.sol (default strategy 1)
At line 2 NAME MODEL
At line 3 ROWS
At line 342 COLUMNS
At line 5815 RHS
At line 6153 BOUNDS
At line 6538 ENDATA
Problem MODEL has 337 rows, 288 columns and 5184 elements
Coin0008I MODEL read with 0 errors
Option for timeMode changed from cpu to elapsed
Continuous objective value is -0.393301 - 0.00 seconds
Cgl0003I 0 fixed, 0 tightened bounds, 71 strengthened rows, 0 substitutions
Cgl0003I 0 fixed, 0 tightened bounds, 1 strengthened rows, 0 substitutions
Cgl0004I processed model has 332 rows, 288 columns (96 integer (96 of which binary)) and 5191 elements
Cbc0038I Initial state - 37 integers unsatisfied sum - 4.91781
Cbc0038I Pass 1: suminf. 4.04198 (36) obj. 1.00374 iterations 77
Cbc0038I Pass 2: suminf. 0.69500 (12) obj. 1.14495 iterations 56
Cbc0038I Solution found of 1.14495
Cbc0038I Relaxing continuous gives 0.954144
Cbc0038I Before mini branch and bound, 39 integers at bound fixed and 115 continuous
Cbc0038I Full problem 332 rows 288 columns, reduced to 21 rows 27 columns
Cbc0038I Mini branch and bound improved solution from 0.954144 to 0.393301 (0.03 seconds)
Cbc0038I Freeing continuous variables gives a solution of 0.393301
Cbc0038I After 0.03 seconds - Feasibility pump exiting with objective of 0.393301 - took 0.01 seconds
Cbc0012I Integer solution of 0.39330141 found by feasibility pump after 0 iterations and 0 nodes (0.03 seconds)
Cbc0001I Search completed - best objective 0.3933014127418696, took 0 iterations and 0 nodes (0.03 seconds)
Cbc0035I Maximum depth 0, 0 variables fixed on reduced cost
Cuts at root node changed objective from 0.393301 to 0.393301
Probing was tried 0 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)
Gomory was tried 0 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)
Knapsack was tried 0 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)
Clique was tried 0 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)
MixedIntegerRounding2 was tried 0 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)
FlowCover was tried 0 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)
TwoMirCuts was tried 0 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)
ZeroHalf was tried 0 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)
Result - Optimal solution found
Objective value: -0.39330141
Enumerated nodes: 0
Total iterations: 0
Time (CPU seconds): 0.03
Time (Wallclock seconds): 0.04
Option for printingOptions changed from normal to all
Total time (CPU seconds): 0.03 (Wallclock seconds): 0.04
2024-04-06 14:32:36,472 - web_server - INFO - Status: Optimal
2024-04-06 14:32:36,472 - web_server - INFO - Total value of the Cost function = -0.39
2024-04-06 14:32:36,702 - web_server - INFO - Passed runtime parameters: {}
2024-04-06 14:32:36,702 - web_server - INFO - >> Setting input data dict
2024-04-06 14:32:36,702 - web_server - INFO - Setting up needed data
2024-04-06 14:32:36,704 - web_server - INFO - >> Publishing data…
2024-04-06 14:32:36,705 - web_server - INFO - Publishing data to HASS instance
2024-04-06 14:32:36,723 - web_server - INFO - Successfully posted to sensor.p_pv_forecast = 2954
2024-04-06 14:32:36,741 - web_server - INFO - Successfully posted to sensor.p_load_forecast = 872
2024-04-06 14:32:36,742 - web_server - ERROR - P_deferrable0 was not found in results DataFrame. Optimization task may need to be relaunched or it did not converge to a solution.
2024-04-06 14:32:36,758 - web_server - INFO - Successfully posted to sensor.p_batt_forecast = -4166.4
2024-04-06 14:32:36,781 - web_server - INFO - Successfully posted to sensor.soc_batt_forecast = 86.62
2024-04-06 14:32:36,794 - web_server - INFO - Successfully posted to sensor.p_grid_forecast = 2084.4
2024-04-06 14:32:36,808 - web_server - INFO - Successfully posted to sensor.total_cost_fun_value = -0.39
2024-04-06 14:32:36,816 - web_server - INFO - Successfully posted to sensor.optim_status = Optimal
2024-04-06 14:32:36,829 - web_server - INFO - Successfully posted to sensor.unit_load_cost = 0.13
2024-04-06 14:32:36,843 - web_server - INFO - Successfully posted to sensor.unit_prod_price = 0.02
Known bug. 0.8.5 might fix it alternatively you’ll have to wait for 24 hours I think.
See DST issues in the CHANGELOG.md file.
1 Like
Warning: I am quite annoyed at the documentation at this point.
I am guessing that I am just stupid, but could someone please explain how I am supposed to define 4 * 430 Watt Panels with a 1.8 kW peak Inverter.
And yes, it’s not an actual PV Installation on a roof, but instead a “Balkonkraftwerk” or small PV System that you can put up on your balcony. It’s plug and play. No strings etc. just one 4 x mppt Input inverter with 1.8 kWp.
Why do I even have to do enter it like this:
-pv_module_model: CSUN_Eurasia_Energy_Systems_Industry_and_Trade_CSUN295_60M
It is “to me” totally unclear how I am supposed to define my panel.
I would do it like this:
-pv_module_model: SUNPROPOWER_SPDG_xxx_-N108M10
Datasheet
Which without even trying I can 10000% accurately say won’t work, because it’s nonsense.
And don’t get me started on my Inverter
Do I just write:
- pv_inverter_model: HMS-1800-4T
I read the documentation, but nowhere does it state how to actually come up with this seemingly Random array of strings. Do I make stuff up? Does it parse it in a specific way? No clue.
It would be 1000 times easier if I could just enter the kWp and orientation of the panels + Inverter settings. I don’t know why I need to enter the exact panel. Does it compute it to a milliwatt accurate? I just have 4 panels with 430 Watt peak each. I don’t need complicated multi array string configurations with 20 inverters and 4000 different panels.
Again, maybe I am stupid and just need to enter 1__222_xxxx__430Wpx4Panels__1xInverter1.8kWp___NaN_X-X-Xxx to configure it.
I feel really stupid to not get how I am supposed to set this up.
Also on a sidenote, what does
“list_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.”
even mean. Is it the minimum state interval for the device? I don’t know what this is supposed to mean.
I am really sorry, but I usually have no problem with Documentation, but this one reads like a cryptic puzzle.
Maybe as an idea. Input Fields for the usual physical characteristics of the Panels and Inverter, then press plus or add a new tab for another string. Add all Voltages, Amps, kWp together, depending on arrangement. Also, I don’t want to calculate the thermal load on the aluminum frame and the resulting performance drop. While we’re at it, why not add the ability to simulate induction losses in loose copper wire from old telephone wires in the ground.
What I am trying to say is: Simpler is usually better.
Again, for anyone reaching this Part. I am really sorry for ranting this much, but I really need to know if anyone thinks this is a good way of configuring this stuff and thinks the documentation is not unclearly written. But I really wanted some actual human feedback because the usual solution of just throwing it against some GPT will definitely not work with this level of cryptic reasoning required.