变量 [t] 的未初始化 NumericValue 对象没有值 - Pyomo

Question

我是 Pyomo 的新手，并试图找到一种方法来优化我的电池算法，其中未定义电池容量 (Smax) 和逆变器尺寸 (Rmax)。

我的目标是找到最小化电池总充电成本的 Smax 和 Rmax 组合。

但我收到此错误： "未初始化的 NumericValue 对象 Ein[0] 没有值"

这是我的代码：

############################################## ######################

       model = ConcreteModel()

       # Define model parameters
       model.T = Set(doc='hour of the period', initialize=df.hour.tolist(), ordered=True)
       model.Rmax = Var( within=PositiveIntegers )
       model.Smax = Var( within=PositiveIntegers )
       model.Ein = Var(model.T, bounds = (0, None))   " Energy charged in battery at period t"
       model.Eout = Var(model.T, bounds = (0, None))   "Energy discharged from battery at period t"
       model.Z = Var(model.T, bounds = (0, None))   "Energy stored in battery at the end of the hour 
       model.L = Var(model.T)  "Energy bought from the grid at every period t"


   # DEFINE ALL CONSTRAINTS OF THE MODEL

   "Initial storage state = 0 and constraint on SOC flow"

   def storage_state(model, t):
       if t == model.T.first():
          return model.Z[t] == 0
       else:
          return (model.Z[t] == (model.Z[t-1] + (model.Ein[t]) - (model.Eout[t])))    
                     
   model.charge_state = Constraint(model.T, rule=storage_state)


   "Max SOC constraint" 
   def SOC_maxconstraint(model, t):
         return model.Z[t] <= model.Smax
   model.SOC_maxconstraint = Constraint(model.T, rule=SOC_maxconstraint)


   "Min SOC constraint"   
   def SOC_minconstraint(model, t):
        return model.Z[t] >= 0
   model.SOC_minconstraint = Constraint(model.T, rule=SOC_minconstraint)


   "Maximum dischage within a single hour"
   def discharge_constraint(model, t):
        return model.Eout[t] <= model.Rmax
   model.discharge = Constraint(model.T, rule=discharge_constraint)

   "Maximum charge within a single hour"   
   def charge_constraint(model, t):
        return model.Ein[t] <= model.Rmax
   model.charge = Constraint(model.T, rule=charge_constraint)


   "Limit discharge to the amount of the charge in the battery"
   def positive_charge(model, t): 
        return model.Eout[t] <= model.Z[t] 
   model.positive_charge = Constraint(model.T, rule=positive_charge)


  "Demand of energy constraint"
  def demand_constraint(model, t):
        return (model.L[t] == (df.loc[t, 'MktDemand'] + (model.Ein[t]) - (model.Eout[t]))) 
  model.demand_constraint = Constraint(model.T, rule=demand_constraint)


  "Non-negativity of total demand"
  def negativity_demand_constraint(model, t):
       return model.L[t] >= 0
  model.negativity_demand_constraint = Constraint(model.T, rule=negativity_demand_constraint)



  ## Objective function 

  original_costs = sum(df.loc[t, 'TOU'] * df.loc[t, 'MktDemand'] for t in model.T)

  model.costs = sum(df.loc[t, 'TOU'] * (df.loc[t, 'MktDemand'] + (model.Ein[t]) - (model.Eout[t])) 
  for t in model.T)

  model.objective = Objective(expr=model.costs, sense=minimize)

  results = SolverFactory('glpk').solve(model)




  print("Profits % =", round((model.objective() / original_costs ) * 100,2))
  print("Smax:", model.Smax.get_values().values())
  print("Rmax:", model.Rmax.get_values().values())

我得到：

  pyomo\core\expr\numvalue.pyx in pyomo.core.expr.numvalue.value()

  ValueError: No value for uninitialized NumericValue object Ein[0]

你们知道我做错了什么吗？

谢谢！

Answer 1

通常这意味着您的求解器没有得到结果。所以基本上如果这是错误，你的模型是不可行的。

也许尝试使用 tee=True 查看求解器信息。

求解器 = SolverFactory('glpk')

结果 = solver.solve(model,tee=True)

如果它表明您的模型不可行，则说明您的约束、界限或其他方面存在问题（我没有查看方程式）

Answer 2

您要构建 ESS 模型吗？我是说储能系统。