Added missing parameter in configuration

CHANGELOG.md

@@ -1,5 +1,15 @@
 # Changelog
 
+## 0.10.0 - Unreleased
+### Improvement
+- Added support for hybrid inverters
+- 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
+### Fix
+- Fixed forecast methods to treat delta_forecast higher than 1 day
+- Fixed solar.forecast wrong interpolation of nan values
+
 ## 0.9.1 - 2024-05-13
 ### Fix
 - Fix patch for issue with paths to modules and inverters database

config_emhass.yaml

@@ -77,6 +77,7 @@ plant_conf:
   - 16 
   strings_per_inverter: # The number of used strings per inverter
   - 1
+  inverter_is_hybrid: False # Set if it is a hybrid inverter (PV+batteries) or not
   Pd_max: 1000 # If your system has a battery (set_use_battery=True), the maximum discharge power in Watts
   Pc_max: 1000 # If your system has a battery (set_use_battery=True), the maximum charge power in Watts
   eta_disch: 0.95 # If your system has a battery (set_use_battery=True), the discharge efficiency

options.json

@@ -134,6 +134,7 @@
       "strings_per_inverter": 1
     }
   ],
+  "inverter_is_hybrid": false,
   "set_use_battery": false,
   "battery_discharge_power_max": 1000,
   "battery_charge_power_max": 1000,

scripts/script_debug_optim.py

@@ -73,14 +73,14 @@
     optim_conf.update({'lp_solver_path': 'empty'})  # set the path to the LP solver, COIN_CMD default is /usr/bin/cbc
     optim_conf.update({'treat_def_as_semi_cont': [True, True]})
     optim_conf.update({'set_def_constant': [False, False]})
-    optim_conf.update({'P_deferrable_nom': [[500.0, 100.0, 100.0, 500.0], 750.0]})
+    # optim_conf.update({'P_deferrable_nom': [[500.0, 100.0, 100.0, 500.0], 750.0]})
 
     optim_conf.update({'set_use_battery': False})
     optim_conf.update({'set_nocharge_from_grid': False})
     optim_conf.update({'set_battery_dynamic': True})
     optim_conf.update({'set_nodischarge_to_grid': True})
 
-    optim_conf.update({'inverter_is_hybrid': False})
+    plant_conf.update({'inverter_is_hybrid': True})
 
     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
@@ -102,7 +102,7 @@
         fig_inputs_dah.show()
 
     vars_to_plot = ['P_deferrable0', 'P_deferrable1','P_grid', 'P_PV', 'P_PV_curtailment']
-    if optim_conf['inverter_is_hybrid']:
+    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']

src/emhass/optimization.py

@@ -212,7 +212,7 @@ def perform_optimization(self, data_opt: pd.DataFrame, P_PV: np.array, P_load: n
         if self.costfun == 'self-consumption':
             SC  = {(i):plp.LpVariable(cat='Continuous',
                                       name="SC_{}".format(i)) for i in set_I}
-        if self.optim_conf['inverter_is_hybrid']:
+        if self.plant_conf['inverter_is_hybrid']:
             P_hybrid_inverter = {(i):plp.LpVariable(cat='Continuous',
                                                     name="P_hybrid_inverter{}".format(i)) for i in set_I}
         P_PV_curtailment = {(i):plp.LpVariable(cat='Continuous', lowBound=0,
@@ -264,7 +264,7 @@ def perform_optimization(self, data_opt: pd.DataFrame, P_PV: np.array, P_load: n
 
         ## Setting constraints
         # The main constraint: power balance
-        if self.optim_conf['inverter_is_hybrid']:
+        if self.plant_conf['inverter_is_hybrid']:
             constraints = {"constraint_main1_{}".format(i) :
                 plp.LpConstraint(
                     e = P_hybrid_inverter[i] - P_def_sum[i] - P_load[i] + P_grid_neg[i] + P_grid_pos[i] ,
@@ -298,7 +298,7 @@ def perform_optimization(self, data_opt: pd.DataFrame, P_PV: np.array, P_load: n
                 P_nom_inverter = inverter.Paco
             else:
                 P_nom_inverter = self.plant_conf['inverter_model']
-        if self.optim_conf['inverter_is_hybrid']:
+        if self.plant_conf['inverter_is_hybrid']:
             constraints.update({"constraint_hybrid_inverter1_{}".format(i) :
                 plp.LpConstraint(
                     e = P_PV[i] - P_PV_curtailment[i] + P_sto_pos[i] + P_sto_neg[i] - P_nom_inverter,
@@ -642,7 +642,7 @@ def create_matrix(input_list, n):
                 SOC_opt.append(SOCinit - SOC_opt_delta[i])
                 SOCinit = SOC_opt[i]
             opt_tp["SOC_opt"] = SOC_opt
-        if self.optim_conf['inverter_is_hybrid']:
+        if self.plant_conf['inverter_is_hybrid']:
             opt_tp["P_hybrid_inverter"] = [P_hybrid_inverter[i].varValue for i in set_I]
         opt_tp["P_PV_curtailment"] = [P_PV_curtailment[i].varValue for i in set_I]
         opt_tp.index = data_opt.index