battery c min is ignored if charge demand p demand is greater than zero

src/optimizer/optimizer.py

@@ -159,11 +159,14 @@ def _setup_variables(self):
         self.variables['p_demand_pen'] = [[None for t in self.time_steps] for i in range(len(self.batteries))]
         # binary variable to allow one out of two alternative constraints
         self.variables['z_p_demand'] = [[None for t in self.time_steps] for i in range(len(self.batteries))]
+        # binary variable to capture when s_max is reached
+        self.variables['z_s_max_reached'] = [[None for t in self.time_steps] for i in range(len(self.batteries))]
         for i, bat in enumerate(self.batteries):
             if bat.p_demand is not None:
                 for t in self.time_steps:
                     self.variables['p_demand_pen'][i][t] = pulp.LpVariable(f"p_demand_pen_{i}_{t}", lowBound=0)
                     self.variables['z_p_demand'][i][t] = pulp.LpVariable(f"z_p_demand_{i}_{t}", cat='Binary')
+                    self.variables['z_s_max_reached'][i][t] = pulp.LpVariable(f"z_s_max_reached_{i}_{t}", cat='Binary')
 
         # penalty variable for staying above max SOC and below min SOC
         self.variables['s_max_pen'] = [[pulp.LpVariable(f"s_max_pen_{i}_{t}", lowBound=0) for t in self.time_steps] for i in range(len(self.batteries))]
@@ -448,25 +451,31 @@ def _add_battery_constraints(self):
             if bat.p_demand is not None:
                 for t in self.time_steps:
                     if bat.p_demand[t] > 0:
-                        # clip required charge to max charging power if needed
-                        # and leave some air to breathe for the optimizer
+                        # clip requested charging power to max charging power if needed
                         p_demand = min(bat.c_max * self.time_series.dt[t] / 3600., bat.p_demand[t])
-                        # two alternative constraints, only one is active:
-                        # constraint option 1: charge energy tries to reach min charge energy parameter
-                        self.problem += (self.variables['c'][i][t] + self.variables['p_demand_pen'][i][t]
-                                         + self.M * self.variables['z_p_demand'][i][t] >= p_demand)
-                        # constraint option 2: charge energy tries to reach energy to fill the battery to s_max
+
+                        # introduce z_p_demand to become only 1 if s_max is almost reached and the
+                        # charging with c_min would stop before the end of the time slot
+                        self.problem += ((bat.s_max - self.variables['s'][i][t])
+                                         <= (1 - self.variables['z_p_demand'][i][t]) * self.M
+                                         + bat.c_min * self.time_series.dt[t] / 3600.)
+
+                        # introduce z_s_max_reached to become only 1 if s_max is fully reached
+                        self.problem += ((bat.s_max - self.variables['s'][i][t])
+                                         <= (1 - self.variables['z_s_max_reached'][i][t]) * self.M)
+
+                        # set a "soft" constraint to reach the requested charging rate if possible.
+                        # deactivate it if s_max is already reached
                         self.problem += (self.variables['c'][i][t] + self.variables['p_demand_pen'][i][t]
-                                         + self.M * (1 - self.variables['z_p_demand'][i][t])
-                                         - (self.batteries[i].s_max - self.variables['s'][i][t]) >= 0.)
-                    elif bat.c_min > 0:
-                        # in time steps without given charging demand, apply normal lower bound:
-                        # Lower bound: either 0 or at least c_min
-                        self.problem += (self.variables['c'][i][t] >= bat.c_min * self.time_series.dt[t] / 3600.
-                                         * self.variables['z_c'][i][t])
+                                         >= (1 - self.variables['z_s_max_reached'][i][t]) * p_demand)
+
+                    if bat.c_min > 0:
+                        # set constraints to exclude charging between 0 and c_min if z_p_demand is not 1.
+                        self.problem += (self.variables['c'][i][t] + self.variables['z_p_demand'][i][t] * self.M
+                                         >= bat.c_min * self.time_series.dt[t] / 3600. * self.variables['z_c'][i][t])
                         self.problem += (self.variables['c'][i][t] <= self.M * self.variables['z_c'][i][t])
 
-            # Constraint (7): Minimum charge power limits if there is not charge demand
+            # Constraint (7): Minimum charge power limits if there is no charge demand
             elif bat.c_min > 0:
                 for t in self.time_steps:
                     # Lower bound: either 0 or at least c_min

test_cases/011-infeasible-charge-demand.json

@@ -336,14 +336,14 @@
             {
                 "charging_power": [
                     2700.3723,
-                    2726.2801,
+                    3775.7398,
                     4087.0091,
                     3515.8202,
-                    2656.4201,
+                    1606.9604,
                     1803.9868,
                     824.5558,
-                    -7.48207e-12,
-                    -2.4940233e-12,
+                    0.0,
+                    0.0,
                     0.0,
                     0.0,
                     0.0,
@@ -434,9 +434,9 @@
                 ],
                 "state_of_charge": [
                     3847.3351,
-                    6300.9872,
-                    9979.2954,
-                    13143.534,
+                    7245.5009,
+                    10923.809,
+                    14088.047,
                     15534.312,
                     17157.9,
                     17900.0,
@@ -485,10 +485,10 @@
             {
                 "charging_power": [
                     0.0,
-                    1049.4597,
                     0.0,
                     0.0,
                     0.0,
+                    1049.4597,
                     0.0,
                     0.0,
                     0.0,
@@ -544,8 +544,8 @@
                     552.01445,
                     689.2983,
                     326.2485,
-                    7.1183464e-11,
-                    -7.1183464e-11,
+                    0.0,
+                    0.0,
                     0.0,
                     0.0,
                     0.0,
@@ -583,9 +583,9 @@
                 ],
                 "state_of_charge": [
                     1267.2,
-                    2211.7137,
-                    2211.7137,
-                    2211.7137,
+                    1267.2,
+                    1267.2,
+                    1267.2,
                     2211.7137,
                     2211.7137,
                     2211.7137,
@@ -732,10 +732,10 @@
         ],
         "flow_direction": [
             0,
-            1,
             0,
             0,
             0,
+            1,
             0,
             0,
             0,