PYOMO: статус невозможного решателя - PullRequest
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PYOMO: статус невозможного решателя

0 голосов
/ 05 июля 2018

У меня проблема с поиском решения для статуса моего решателя из-за того, что я хочу найти оптимальное решение для моего LP. Ниже вы можете найти код проблемы, которую я хочу решить. Я понятия не имею, почему оптимизатор не может найти оптимальное решение. Поскольку я новичок в программировании, я очень ценю любую помощь. Заранее спасибо !!

`

import pyomo.environ as pe
from Timer import Timer
from Pvsystem import Pvsystem
from Loadprofile import Loadprofile

#model object
LPopt = pe.ConcreteModel()

#objects:
my_timer = Timer()
my_pv = Pvsystem()
my_load = Loadprofile()

#define 'index sets'
LPopt.T = pe.RangeSet(0, 8759, 1)

#declare pv_power as a variable
LPopt.pv_power = pe.Var(LPopt.T, domain=pe.NonNegativeReals) 
#define constraint list for pv output
LPopt.pv_constraint = pe.ConstraintList()
pv_value = (my_pv.get_total_pv_output()) #in kW
#add constraints to the pv constraint list
for t in range(my_timer.get_total_time_steps()):
    LPopt.pv_constraint.add(float(pv_value[t]) == LPopt.pv_power[t])

#declare load_power as a variable
LPopt.load_power = pe.Var(LPopt.T, domain=pe.NonNegativeReals)
#define constraint list for load
LPopt.load_constraint = pe.ConstraintList()
load_value = (my_load.get_load_profile()) #in kW

#add constraints to the load constraint list
for t in range(my_timer.get_total_time_steps()):
    LPopt.load_constraint.add(float(load_value[t]) == LPopt.load_power[t])

#parameters
#maximum battery capacity of the storage device
LPopt.max_bat_capacity = pe.Param(default=750, within=pe.NonNegativeReals) #in kWh

#battery charge power efficiency
LPopt.eta_bat_ch = pe.Param(default=0.975, within=pe.NonNegativeReals) #unitless

#limited grid import power
LPopt.max_grid_import_power = pe.Param(default=4500.0, within=pe.NonNegativeReals)
#limited grid export power
LPopt.max_grid_export_power = pe.Param(default=1000.0, within=pe.NonNegativeReals)

#variables
#battery input power as a variable indexed by the set T
LPopt.bat_ch_power = pe.Var(LPopt.T, domain=pe.NonNegativeReals, initialize=0.0)
#battery charge power as a variable indexed by the set T
LPopt.bat_dch_power = pe.Var(LPopt.T, domain=pe.NonNegativeReals, initialize=0.0)
#battery state energy as a variable indexed by the set T
LPopt.bat_energy = pe.Var(LPopt.T, domain=pe.NonNegativeReals, initialize=0.0)
#previous battery content
LPopt.bat_energy_prev = pe.Var(LPopt.T, domain=pe.NonNegativeReals, initialize=0.0)

#grid import power as a variable indexed by the set T
LPopt.grid_import_power = pe.Var(LPopt.T, domain=pe.NonNegativeReals, initialize=0.0)
#grid export power as a variable indexed by the set T
LPopt.grid_export_power = pe.Var(LPopt.T, domain=pe.NonNegativeReals, initialize=0.0)

#create the objective function
LPopt.value = pe.Objective(expr=sum(LPopt.grid_import_power[t] + LPopt.grid_export_power[t] for t in LPopt.T), sense=pe.minimize)

#constraints
#grid constraint
LPopt.grid_import_constraint = pe.ConstraintList()
for t in range(my_timer.get_total_time_steps()):
    LPopt.grid_import_constraint.add(LPopt.grid_import_power[t] <= LPopt.max_grid_import_power)

LPopt.grid_export_constraint = pe.ConstraintList()
for t in range(my_timer.get_total_time_steps()):
    LPopt.grid_export_constraint.add(LPopt.grid_export_power[t] <= LPopt.max_grid_export_power)

LPopt.grid_constraint = pe.ConstraintList()
for t in range(my_timer.get_total_time_steps()):
    LPopt.grid_constraint.add(LPopt.grid_import_power[t] - LPopt.grid_export_power[t] == LPopt.load_power[t] + LPopt.bat_ch_power[t] - LPopt.bat_dch_power[t] - LPopt.pv_power[t])

#battery energy constraint
LPopt.bat_energy_constraint = pe.ConstraintList()
for t in range(my_timer.get_total_time_steps()):
    if t == 0:
        LPopt.bat_energy_constraint.add(LPopt.bat_energy_prev[t] == 0.0)
    else:
        LPopt.bat_energy_constraint.add(LPopt.bat_energy_prev[t] == LPopt.bat_energy[t-1])
    LPopt.bat_energy_constraint.add(LPopt.bat_energy[t] == LPopt.bat_energy_prev[t] + my_timer.get_deltaT() * (LPopt.eta_bat_ch * LPopt.bat_ch_power[t] - LPopt.bat_dch_power[t]))

#battery capacity constraint        
LPopt.battery_capacity_constraint = pe.ConstraintList()
for t in range(my_timer.get_total_time_steps()):
    LPopt.battery_capacity_constraint.add(LPopt.bat_energy[t] <= LPopt.max_bat_capacity)

# Optimize
solver = pe.SolverFactory('cbc')
status = solver.solve(LPopt)

# Print the status of the solved LP
print("Status = %s" % status.solver.termination_condition)

`

Я предполагаю, что одним из моих ограничений / или самой цели может быть проблема. Надеюсь получить помощь от вас!

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