def opt_control_minCost(self):
# Instantiate optimization problem
self.opt_problem = optimization.Optimization(self.mpc,
optimization.EnergyCostMin,
optimization.JModelica,
self.target_variable,
constraint_data = self.constraints.data,
add_objective = self.slack_var
)
print("%%%%%%%%%%%%%%%----Starting Optimization of Control ---%%%%%%%%%%%%%")
self.opt_options = {}
self.opt_options['blocking_factors'] = [1]*10
self.opt_problem.set_optimization_options(self.opt_options)
self.opt_problem.optimize(self.opt_start,self.opt_end,
price_data = self.price.data,
rho = self.rho.data,
addobj = self.addobj.data
)
print("----Optimization stats----")
print(self.opt_problem.get_optimization_statistics())
print("----Optimization outcome----")
self.opt_controlseq = self.opt_problem.Model.control_data # dictionary with mpcpy-timeseries variables of controls
store_namespace('opt_control_mincost_'+self.building,self.opt_controlseq)
def opt_control_minDRCost(self):
# Instantiate optimization problem
self.opt_problem = optimization.Optimization(self.mpc,
optimization.DRCostMin,
optimization.JModelica,
self.target_variable,
constraint_data = self.constraints.data,
add_objective = self.slack_var
)
print("%%%%%%%%%%%%%%%----Starting Optimization of Control ---%%%%%%%%%%%%%")
self.opt_options = {}
self.opt_options['IPOPT_options']={}
self.opt_options['IPOPT_options']['obj_scaling_factor'] = 100
self.opt_options['n_cp'] = 1
self.opt_problem.set_optimization_options(self.opt_options)
self.opt_problem.optimize(self.opt_start,self.opt_end,
price_data = self.price.data,
ref_profile = self.ref_profile.data,
flex_cost = self.flex_cost.data,
rho = self.rho.data,
addobj = self.addobj.data
)
print("----Optimization stats----")
print(self.opt_problem.get_optimization_statistics())
print("----Optimization outcome----")
self.opt_controlseq = self.opt_problem.Model.control_data # dictionary with mpcpy-timeseries variables of controls
store_namespace('opt_control_DRcost_'+self.building,self.opt_controlseq)
def opt_control_minEnergy(self):
# Instantiate optimization problem
self.opt_problem = optimization.Optimization(self.mpc,
optimization.EnergyMin,
optimization.JModelica,
self.target_variable,
constraint_data = self.constraints.data
)
self.opt_options = {}
self.opt_options['blocking_factors'] = [1]*10
self.opt_problem.set_optimization_options(self.opt_options)
#self.opt_problem
print("%%%%%%%%%%%%%%%----Starting Optimization of Control ---%%%%%%%%%%%%%")
self.opt_problem.optimize(self.opt_start,self.opt_end,res_control_step=self.meas_sampl)
print("----Optimization stats----")
print(self.opt_problem.get_optimization_statistics())
print("----Optimization outcome----")
self.opt_controlseq = self.opt_problem.Model.control_data # dictionary with mpcpy-timeseries variables of controls
print(self.opt_controlseq)
store_namespace('opt_control_minenergy_'+self.building,self.opt_controlseq)