Merge pull request #298 from davidusb-geek/davidusb-geek/patch/fix_v0…

…p10p1

Fixed PV curtailment limitation and publish

CHANGELOG.md

@@ -1,8 +1,16 @@
 # Changelog
 
+## 0.10.1 - 2024-06-03
+### Fix
+- Fixed PV curtailment maximum possible value constraint
+- Added PV curtailement to variable to publish to HA
+
 ## 0.10.0 - 2024-06-02
+### BREAKING CHANGE
+- In this new version we have added support for PV curtailment computation. While doing this the nominal PV peak power is needed. The easiest way find this information is by directly using the `inverter_model` defined in the configuration. As this is needed in the optimization to correctly compute PV curtailment, this parameter need to be properly defined for your installation. Before this chage this parameter was only needed if using the PV forecast method `scrapper`, but now it is not optional as it is directly used in the optimization. 
+Use the dedicated webapp to find the correct model for your inverter, if you cannot find your exact brand/model then just pick an inverter with the same nominal power as yours: [https://emhass-pvlib-database.streamlit.app/](https://emhass-pvlib-database.streamlit.app/)
 ### Improvement
-- Added support for hybrid inverters
+- Added support for hybrid inverters and PV curtailment computation
 - Implemented a new `continual_publish` service that avoid the need of setting a special automation for data publish. Thanks to @GeoDerp
 - Implement a deferrable load start penalty functionality. Thanks to @werdnum
   - This feature also implement a `def_current_state` that can be passed at runtime to let the optimization consider that a deferrable load is currently scheduled or under operation when launching the optimization task

data/opt_res_latest.csv

@@ -1,11 +1,49 @@
-timestamp,P_PV,P_Load,P_deferrable0,P_deferrable1,P_grid_pos,P_grid_neg,P_grid,unit_load_cost,unit_prod_price,cost_profit,cost_fun_profit
-2023-01-27 10:30:00+01:00,0.5,1127.6172187500001,3000.0,750.0,4877.1172,0.0,4877.1172,1,1,-2.4385586,-2.4385586
-2023-01-27 11:00:00+01:00,2.0,2.0,3000.0,750.0,3750.0,0.0,3750.0,2,2,-3.75,-3.75
-2023-01-27 11:30:00+01:00,3.0,3.0,3000.0,750.0,3750.0,0.0,3750.0,3,3,-5.625,-5.625
-2023-01-27 12:00:00+01:00,4.0,4.0,3000.0,750.0,3750.0,0.0,3750.0,4,4,-7.5,-7.5
-2023-01-27 12:30:00+01:00,5.0,5.0,3000.0,750.0,3750.0,0.0,3750.0,5,5,-9.375,-9.375
-2023-01-27 13:00:00+01:00,6.0,6.0,3000.0,750.0,3750.0,0.0,3750.0,6,6,-11.25,-11.25
-2023-01-27 13:30:00+01:00,7.0,7.0,3000.0,750.0,3750.0,0.0,3750.0,7,7,-13.125,-13.125
-2023-01-27 14:00:00+01:00,8.0,8.0,3000.0,750.0,3750.0,0.0,3750.0,8,8,-15.0,-15.0
-2023-01-27 14:30:00+01:00,9.0,9.0,6000.0,750.0,6750.0,0.0,6750.0,9,9,-30.375,-30.375
-2023-01-27 15:00:00+01:00,10.0,10.0,0.0,5250.0,5250.0,0.0,5250.0,10,10,-26.25,-26.25
+timestamp,P_PV,P_Load,P_deferrable0,P_deferrable1,P_grid_pos,P_grid_neg,P_grid,P_PV_curtailment,unit_load_cost,unit_prod_price,cost_profit,cost_fun_profit,optim_status
+2024-06-03 23:00:00+02:00,0.0,2124.8911797752808,0.0,750.0,2874.8912,0.0,2874.8912,0.0,0.1419,0.065,-0.20397353064,-0.20397353064,Optimal
+2024-06-03 23:30:00+02:00,0.0,393.7693220338983,3000.0,750.0,4143.7693,0.0,4143.7693,0.0,0.1419,0.065,-0.29400043183499996,-0.29400043183499996,Optimal
+2024-06-04 00:00:00+02:00,0.0,329.5656571428571,0.0,750.0,1079.5657,0.0,1079.5657,0.0,0.1419,0.065,-0.076595186415,-0.076595186415,Optimal
+2024-06-04 00:30:00+02:00,0.0,214.95473988439306,0.0,750.0,964.95474,0.0,964.95474,0.0,0.1419,0.065,-0.068463538803,-0.068463538803,Optimal
+2024-06-04 01:00:00+02:00,0.0,254.92180790960455,3000.0,0.0,3254.9218,0.0,3254.9218,0.0,0.1419,0.065,-0.23093670171,-0.23093670171,Optimal
+2024-06-04 01:30:00+02:00,0.0,653.0385393258427,0.0,0.0,653.03854,0.0,653.03854,0.0,0.1419,0.065,-0.046333084413000006,-0.046333084413000006,Optimal
+2024-06-04 02:00:00+02:00,0.0,694.8668181818182,0.0,0.0,694.86682,0.0,694.86682,0.0,0.1419,0.065,-0.049300800879,-0.049300800879,Optimal
+2024-06-04 02:30:00+02:00,0.0,856.8446739130435,0.0,0.0,856.84467,0.0,856.84467,0.0,0.1419,0.065,-0.060793129336499996,-0.060793129336499996,Optimal
+2024-06-04 03:00:00+02:00,0.0,914.380597826087,0.0,0.0,914.3806,0.0,914.3806,0.0,0.1907,0.065,-0.08718619021000001,-0.08718619021000001,Optimal
+2024-06-04 03:30:00+02:00,0.0,599.8399421965318,0.0,0.0,599.83994,0.0,599.83994,0.0,0.1907,0.065,-0.057194738279,-0.057194738279,Optimal
+2024-06-04 04:00:00+02:00,0.0,703.5027607361963,0.0,0.0,703.50276,0.0,703.50276,0.0,0.1907,0.065,-0.067078988166,-0.067078988166,Optimal
+2024-06-04 04:30:00+02:00,0.0,646.7419879518072,0.0,0.0,646.74199,0.0,646.74199,0.0,0.1907,0.065,-0.061666848746500004,-0.061666848746500004,Optimal
+2024-06-04 05:00:00+02:00,0.0,1009.152816091954,0.0,0.0,1009.1528,0.0,1009.1528,0.0,0.1907,0.065,-0.09622271948,-0.09622271948,Optimal
+2024-06-04 05:30:00+02:00,0.0,967.1363841807911,0.0,0.0,967.13638,0.0,967.13638,0.0,0.1907,0.065,-0.092216453833,-0.092216453833,Optimal
+2024-06-04 06:00:00+02:00,0.0,935.1571508379889,0.0,0.0,935.15715,0.0,935.15715,0.0,0.1907,0.065,-0.0891672342525,-0.0891672342525,Optimal
+2024-06-04 06:30:00+02:00,60.0,3267.5106703910615,0.0,0.0,3207.5107,0.0,3207.5107,0.0,0.1907,0.065,-0.305836145245,-0.305836145245,Optimal
+2024-06-04 07:00:00+02:00,840.0,3286.2027777777776,0.0,0.0,2446.2028,0.0,2446.2028,0.0,0.1907,0.065,-0.23324543698000003,-0.23324543698000003,Optimal
+2024-06-04 07:30:00+02:00,660.0,1496.1914772727273,0.0,0.0,836.19148,0.0,836.19148,0.0,0.1907,0.065,-0.079730857618,-0.079730857618,Optimal
+2024-06-04 08:00:00+02:00,620.0,794.2991620111732,0.0,0.0,174.29916,0.0,174.29916,0.0,0.1907,0.065,-0.016619424906,-0.016619424906,Optimal
+2024-06-04 08:30:00+02:00,620.0,832.2424719101124,0.0,0.0,212.24247,0.0,212.24247,0.0,0.1907,0.065,-0.020237319514500002,-0.020237319514500002,Optimal
+2024-06-04 09:00:00+02:00,6380.0,788.9761235955057,3000.0,750.0,0.0,-1841.0239,-1841.0239,0.0,0.1907,0.065,0.05983327675,0.05983327675,Optimal
+2024-06-04 09:30:00+02:00,1095.5620000000001,781.2152298850575,0.0,750.0,435.65323,0.0,435.65323,0.0,0.1907,0.065,-0.0415395354805,-0.0415395354805,Optimal
+2024-06-04 10:00:00+02:00,811.4380000000002,664.0545197740113,0.0,0.0,0.0,-147.38348,-147.38348,0.0,0.1907,0.065,0.0047899631,0.0047899631,Optimal
+2024-06-04 10:30:00+02:00,681.0759999999999,666.1989265536723,0.0,0.0,0.0,-14.877073,-14.877073,0.0,0.1907,0.065,0.00048350487250000003,0.00048350487250000003,Optimal
+2024-06-04 11:00:00+02:00,671.9846666666667,669.4183146067417,0.0,0.0,0.0,-2.5663521,-2.5663521,0.0,0.1907,0.065,8.340644325000001e-05,8.340644325000001e-05,Optimal
+2024-06-04 11:30:00+02:00,6469.634666666666,579.2235294117647,3000.0,750.0,0.0,0.0,0.0,2140.4111,0.1907,-0.07,-0.0,-0.0,Optimal
+2024-06-04 12:00:00+02:00,2992.012,642.7344318181817,3000.0,0.0,650.72243,0.0,650.72243,0.0,0.1907,-0.07,-0.06204638370050001,-0.06204638370050001,Optimal
+2024-06-04 12:30:00+02:00,1867.9053333333331,637.1688636363637,0.0,750.0,0.0,0.0,0.0,480.73647,0.1907,-0.07,-0.0,-0.0,Optimal
+2024-06-04 13:00:00+02:00,2067.554666666667,649.3890173410405,3000.0,0.0,1581.8344,0.0,1581.8344,0.0,0.1907,-0.07,-0.15082791004,-0.15082791004,Optimal
+2024-06-04 13:30:00+02:00,622.756,509.79664739884396,0.0,0.0,0.0,0.0,0.0,112.95935,0.1907,-0.07,-0.0,-0.0,Optimal
+2024-06-04 14:00:00+02:00,1518.7553333333335,500.53686046511626,0.0,750.0,0.0,-268.21847,-268.21847,0.0,0.1907,0.065,0.008717100275000002,0.008717100275000002,Optimal
+2024-06-04 14:30:00+02:00,2551.502,520.944,3000.0,0.0,969.442,0.0,969.442,0.0,0.1907,0.065,-0.0924362947,-0.0924362947,Optimal
+2024-06-04 15:00:00+02:00,4160.0,415.32341040462427,3000.0,750.0,5.3234104,0.0,5.3234104,0.0,0.1907,0.065,-0.00050758718164,-0.00050758718164,Optimal
+2024-06-04 15:30:00+02:00,4240.0,321.6410285714286,3000.0,750.0,0.0,-168.35897,-168.35897,0.0,0.1419,0.065,0.005471666525,0.005471666525,Optimal
+2024-06-04 16:00:00+02:00,560.0,3778.0933888888894,3000.0,750.0,6968.0934,0.0,6968.0934,0.0,0.1419,0.065,-0.49438622673,-0.49438622673,Optimal
+2024-06-04 16:30:00+02:00,380.0,3990.816179775281,0.0,750.0,4360.8162,0.0,4360.8162,0.0,0.1419,0.065,-0.30939990939,-0.30939990939,Optimal
+2024-06-04 17:00:00+02:00,0.0,3909.8039890710384,0.0,750.0,4659.804,0.0,4659.804,0.0,0.1419,0.065,-0.33061309380000004,-0.33061309380000004,Optimal
+2024-06-04 17:30:00+02:00,0.0,4206.869447513812,0.0,0.0,4206.8694,0.0,4206.8694,0.0,0.1907,0.065,-0.40112499728999995,-0.40112499728999995,Optimal
+2024-06-04 18:00:00+02:00,0.0,486.26584269662925,0.0,0.0,486.26584,0.0,486.26584,0.0,0.1907,0.065,-0.046365447844000006,-0.046365447844000006,Optimal
+2024-06-04 18:30:00+02:00,0.0,402.43446927374305,0.0,0.0,402.43447,0.0,402.43447,0.0,0.1907,0.065,-0.0383721267145,-0.0383721267145,Optimal
+2024-06-04 19:00:00+02:00,0.0,316.17875,0.0,0.0,316.17875,0.0,316.17875,0.0,0.1907,0.065,-0.0301476438125,-0.0301476438125,Optimal
+2024-06-04 19:30:00+02:00,0.0,867.4,0.0,0.0,867.4,0.0,867.4,0.0,0.1907,0.065,-0.08270659000000001,-0.08270659000000001,Optimal
+2024-06-04 20:00:00+02:00,0.0,340.8070760233918,0.0,0.0,340.80708,0.0,340.80708,0.0,0.1907,0.065,-0.032495955078,-0.032495955078,Optimal
+2024-06-04 20:30:00+02:00,0.0,349.07406779661017,0.0,750.0,1099.0741,0.0,1099.0741,0.0,0.1419,0.065,-0.077979307395,-0.077979307395,Optimal
+2024-06-04 21:00:00+02:00,0.0,1790.5224581005587,0.0,750.0,2540.5225,0.0,2540.5225,0.0,0.1419,0.065,-0.18025007137500001,-0.18025007137500001,Optimal
+2024-06-04 21:30:00+02:00,0.0,2612.0882857142856,0.0,0.0,2612.0883,0.0,2612.0883,0.0,0.1419,0.065,-0.185327664885,-0.185327664885,Optimal
+2024-06-04 22:00:00+02:00,0.0,2617.098882681564,0.0,0.0,2617.0989,0.0,2617.0989,0.0,0.1419,0.065,-0.185683166955,-0.185683166955,Optimal
+2024-06-04 22:30:00+02:00,0.0,2254.2344375000002,0.0,0.0,2254.2344,0.0,2254.2344,0.0,0.1419,0.065,-0.15993793068,-0.15993793068,Optimal

docs/conf.py

@@ -22,7 +22,7 @@
 author = 'David HERNANDEZ'
 
 # The full version, including alpha/beta/rc tags
-release = '0.10.0'
+release = '0.10.1'
 
 # -- General configuration ---------------------------------------------------
 

scripts/script_debug_optim.py

@@ -80,7 +80,7 @@
     optim_conf.update({'set_battery_dynamic': True})
     optim_conf.update({'set_nodischarge_to_grid': True})
 
-    plant_conf.update({'inverter_is_hybrid': True})
+    plant_conf.update({'inverter_is_hybrid': False})
 
     df_input_data.loc[df_input_data.index[25:30],'unit_prod_price'] = -0.07
     df_input_data['P_PV_forecast'] = df_input_data['P_PV_forecast']*2
@@ -105,7 +105,7 @@
     if plant_conf['inverter_is_hybrid']:
         vars_to_plot = vars_to_plot + ['P_hybrid_inverter']
     if optim_conf['set_use_battery']:
-        vars_to_plot = vars_to_plot + ['P_batt']
+        vars_to_plot = vars_to_plot + ['P_batt'] + ['SOC_opt']
     fig_res_dah = opt_res_dayahead[vars_to_plot].plot() # 'P_def_start_0', 'P_def_start_1', 'P_def_bin2_0', 'P_def_bin2_1'
     fig_res_dah.layout.template = template
     fig_res_dah.update_yaxes(title_text = "Powers (W)")
@@ -115,4 +115,6 @@
 
     print("System with: PV, two deferrable loads, dayahead optimization, profit >> total cost function sum: "+\
         str(opt_res_dayahead['cost_profit'].sum())+", Status: "+opt_res_dayahead['optim_status'].unique().item())
+
+    print(opt_res_dayahead[vars_to_plot])
 

setup.py

@@ -19,7 +19,7 @@
 
 setup(
     name='emhass',  # Required
-    version='0.10.0',  # Required
+    version='0.10.1',  # Required
     description='An Energy Management System for Home Assistant',  # Optional
     long_description=long_description,  # Optional
     long_description_content_type='text/markdown',  # Optional (see note above)

src/emhass/command_line.py

@@ -52,7 +52,7 @@ def set_input_data_dict(emhass_conf: dict, costfun: str,
     logger.info("Setting up needed data")
     # Parsing yaml
     retrieve_hass_conf, optim_conf, plant_conf = utils.get_yaml_parse(
-        emhass_conf, use_secrets=not (get_data_from_file), params=params)
+        emhass_conf, use_secrets=not(get_data_from_file), params=params)
     # Treat runtimeparams
     params, retrieve_hass_conf, optim_conf, plant_conf = utils.treat_runtimeparams(
         runtimeparams, params, retrieve_hass_conf, optim_conf, plant_conf, set_type, logger)
@@ -683,8 +683,6 @@ def regressor_model_predict(input_data_dict: dict, logger: logging.Logger,
             type_var="mlregressor")
     return prediction
 
-
-
 def publish_data(input_data_dict: dict, logger: logging.Logger, 
                  save_data_to_file: Optional[bool] = False, 
                  opt_res_latest: Optional[pd.DataFrame] = None, 
@@ -708,12 +706,10 @@ def publish_data(input_data_dict: dict, logger: logging.Logger,
 
     """
     logger.info("Publishing data to HASS instance")
-
     if not isinstance(input_data_dict["params"],dict):
         params = json.loads(input_data_dict["params"])
     else:
         params = input_data_dict["params"]
-
     # Check if a day ahead optimization has been performed (read CSV file)
     if save_data_to_file:
         today = datetime.now(timezone.utc).replace(
@@ -726,7 +722,6 @@ def publish_data(input_data_dict: dict, logger: logging.Logger,
         opt_res_list_names = []
         publish_prefix = params["passed_data"]["publish_prefix"]
         entity_path = input_data_dict['emhass_conf']['data_path'] / "entities"
-
         # Check if items in entity_path
         if os.path.exists(entity_path) and len(os.listdir(entity_path)) > 0:
             # Obtain all files in entity_path
@@ -805,6 +800,20 @@ def publish_data(input_data_dict: dict, logger: logging.Logger,
         dont_post=dont_post
     )
     cols_published = ["P_PV", "P_Load"]
+    # Publish PV curtailment
+    custom_pv_curtailment_id = params["passed_data"]["custom_pv_curtailment_id"]
+    input_data_dict["rh"].post_data(
+        opt_res_latest["P_PV_curtailment"],
+        idx_closest,
+        custom_pv_curtailment_id["entity_id"],
+        custom_pv_curtailment_id["unit_of_measurement"],
+        custom_pv_curtailment_id["friendly_name"],
+        type_var="power",
+        publish_prefix=publish_prefix,
+        save_entities=entity_save,
+        dont_post=dont_post
+    )
+    cols_published = cols_published + ["P_PV_curtailment"]
     # Publish deferrable loads
     custom_deferrable_forecast_id = params["passed_data"][
         "custom_deferrable_forecast_id"
@@ -944,7 +953,7 @@ def publish_data(input_data_dict: dict, logger: logging.Logger,
         opt_res_latest.index[idx_closest]]]
     return opt_res
 
-def continual_publish(input_data_dict,entity_path,logger):
+def continual_publish(input_data_dict: dict, entity_path: pathlib.Path, logger: logging.Logger):
     """
     If continual_publish is true and a entity file saved in /data_path/entities, continually publish sensor on freq rate, updating entity current state value based on timestamp
 
@@ -959,23 +968,22 @@ def continual_publish(input_data_dict,entity_path,logger):
     logger.info("Continual publish thread service started")
     freq = input_data_dict['retrieve_hass_conf'].get("freq", pd.to_timedelta(1, "minutes"))
     entity_path_contents = []
-
     while True:
         # Sleep for x seconds (using current time as a reference for time left)
         time.sleep(max(0,freq.total_seconds() - (datetime.now(input_data_dict["retrieve_hass_conf"]["time_zone"]).timestamp() % 60)))
-
         # Loop through all saved entity files
         if os.path.exists(entity_path) and len(os.listdir(entity_path)) > 0:
             entity_path_contents =  os.listdir(entity_path)    
             for entity in entity_path_contents:
                 if entity != "metadata.json":
                     # Call publish_json with entity file, build entity, and publish                     
-                    publish_json(entity,input_data_dict,entity_path,logger,"continual_publish")
+                    publish_json(entity, input_data_dict, entity_path, logger, "continual_publish")
         pass 
     # This function should never return           
     return False 
 
-def publish_json(entity,input_data_dict,entity_path,logger,reference: Optional[str] = ""):
+def publish_json(entity: dict, input_data_dict: dict, entity_path: pathlib.Path, 
+                 logger: logging.Logger, reference: Optional[str] = ""):
     """
     Extract saved entity data from .json (in data_path/entities), build entity, post results to post_data
 
@@ -989,9 +997,8 @@ def publish_json(entity,input_data_dict,entity_path,logger,reference: Optional[s
     :type logger: logging.Logger
     :param reference: String for identifying who ran the function  
     :type reference: str, optional
-
+    
     """
-
     # Retrieve entity metadata from file
     if os.path.isfile(entity_path / "metadata.json"):
         with open(entity_path / "metadata.json", "r") as file:
@@ -1001,16 +1008,12 @@ def publish_json(entity,input_data_dict,entity_path,logger,reference: Optional[s
     else:
         logger.error("unable to located metadata.json in:" + entity_path)
         return False            
-
     # Round current timecode (now)
     now_precise = datetime.now(input_data_dict["retrieve_hass_conf"]["time_zone"]).replace(second=0, microsecond=0)
-
     # Retrieve entity data from file
     entity_data = pd.read_json(entity_path / entity , orient='index')
-
     # Remove ".json" from string for entity_id
     entity_id = entity.replace(".json", "")
-
     # Adjust Dataframe from received entity json file
     entity_data.columns = [metadata[entity_id]["name"]]
     entity_data.index.name = "timestamp"
@@ -1025,15 +1028,13 @@ def publish_json(entity,input_data_dict,entity_path,logger,reference: Optional[s
         idx_closest = entity_data.index.get_indexer([now_precise], method="bfill")[0]
     if idx_closest == -1:
         idx_closest = entity_data.index.get_indexer([now_precise], method="nearest")[0]
-
     # Call post data 
     if reference == "continual_publish":
         logger.debug("Auto Published sensor:")
         logger_levels = "DEBUG"
     else: 
         logger_levels = "INFO"
-
-    #post/save entity
+    # post/save entity
     input_data_dict["rh"].post_data(
         data_df=entity_data[metadata[entity_id]["name"]],
         idx=idx_closest,