Imp - Added option to ## [0.2.11] - 2022-04-28

CHANGELOG.md

@@ -4,6 +4,10 @@ All notable changes to this project will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
+## [0.2.11] - 2022-04-28
+### Improvement
+- Added config parameter to consider that all PV power is injected to the grid
+
 ## [0.2.11] - 2022-04-28
 ### Fix
 - Fixed wrong handling of DateTimeIndex when dealing with forecast method for list of values and csv read.

config_emhass.json

@@ -11,7 +11,8 @@
 		{"var_interp": ["sensor.power_photovoltaics", "sensor.power_load_no_var_loads"]}
 	], 
 	"optim_conf": 
-	[
+	[	
+		{"set_total_pv_sell": false},
 		{"set_use_battery": false}, 
 		{"delta_forecast": 1}, 
 		{"num_def_loads": 2}, 

config_emhass.yaml

@@ -14,6 +14,7 @@ retrieve_hass_conf:
     - 'sensor.power_load_no_var_loads'
 
 optim_conf:
+  - set_total_pv_sell: False # consider that all PV power is injected to the grid (self-consumption with total sell)
   - set_use_battery: False # consider a battery storage
   - delta_forecast: 1 # days
   - num_def_loads: 2

setup.py

@@ -19,7 +19,7 @@
 
 setup(
     name='emhass',  # Required
-    version='0.2.11',  # Required
+    version='0.2.12',  # 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/optimization.py

@@ -159,12 +159,21 @@ def perform_optimization(self, data_opt: pd.DataFrame, P_PV: np.array,
         for i in set_I:
             P_def_sum.append(plp.lpSum(P_deferrable[k][i] for k in range(self.optim_conf['num_def_loads'])))
         if self.costfun == 'profit':
-            objective = plp.lpSum(-0.001*self.timeStep*(unit_load_cost[i]*(P_load[i] + P_def_sum[i]) + \
-                                                        unit_prod_price[i] * P_grid_neg[i])
-                                  for i in set_I)
+            if self.optim_conf['set_total_pv_sell']:
+                objective = plp.lpSum(-0.001*self.timeStep*(unit_load_cost[i]*(P_load[i] + P_def_sum[i]) + \
+                                                            unit_prod_price[i]*P_grid_neg[i])
+                                      for i in set_I)
+            else:
+                objective = plp.lpSum(-0.001*self.timeStep*(unit_load_cost[i]*P_grid_pos[i] + \
+                                                            unit_prod_price[i]*P_grid_neg[i])
+                                      for i in set_I)
         elif self.costfun == 'cost':
-            objective = plp.lpSum(-0.001*self.timeStep*unit_load_cost[i]*(P_load[i] + P_def_sum[i])
-                                  for i in set_I)
+            if self.optim_conf['set_total_pv_sell']:
+                objective = plp.lpSum(-0.001*self.timeStep*unit_load_cost[i]*P_grid_pos[i]
+                                      for i in set_I)
+            else:
+                objective = plp.lpSum(-0.001*self.timeStep*unit_load_cost[i]*(P_load[i] + P_def_sum[i])
+                                      for i in set_I)
         elif self.costfun == 'self-consumption':
             objective = plp.lpSum(0.001*self.timeStep*unit_load_cost[i]*SC[i] for i in set_I)
         else: