Pyomo电池优化值错误：未初始化的NumericValue对象没有值

import numpy as np import pandas as pd from pyomo.environ import * from pulp import * power = pd.read_csv('Battery 2 Hourly Power flow for the month with time.csv') solar_forecast = list(power['Solar_(W)']) demand = list(power['Household_(W)']*(-1.0)) power['Time'] = np.arange(720)

Hour Grid_(W) Household_(W) Solar_(W) Storage_(W) Time 00:00 392.095000 -379.960833 0.0 -12.134167 0 01:00 205.441667 -193.115833 0.0 -12.325000 1 02:00 142.570000 -130.807500 0.0 -11.763333 2 03:00 96.456667 -84.829167 0.0 -11.632500 3 04:00 100.940833 -89.330833 0.0 -11.610833 4 05:00 132.357500 -120.559167 0.0 -11.797500 5 06:00 182.733333 -171.605833 0.000000 -11.126667 6 07:00 96.900833 -127.610000 42.106667 -11.397500 7 08:00 35.052500 -228.003333 304.839167 -111.890000 8 09:00 1.385833 -308.922500 489.586667 -182.050833 9 10:00 2086.825833 -2596.776667 492.475000 17.478333 10 11:00 959.740000 -1696.840833 846.346667 -109.245833 11 12:00 2648.243333 -3006.825000 374.156667 -15.579167 12 13:00 1826.446667 -2055.055833 240.062500 -11.453333 13 14:00 1410.041667 -1551.870000 153.488333 -11.658333 14 15:00 536.535000 -572.946667 48.553333 -12.143333 15 16:00 1079.993333 -1069.868333 0.890000 -11.016667 16 17:00 2269.711667 -2260.661667 0.000000 -9.050833 17

model = ConcreteModel() # Define model parameters model.T = Set(initialize=power.Time.tolist(), ordered=True, doc='hour of week') model.Ein = Param(initialize=2400, doc='Max rate of power flow (W) in') model.Eout = Param(initialize=-2400, doc='Max rate of power flow (W) out') model.Smax = Param(initialize=4800, doc='Max storage (Wh)') model.Smin = Param(initialize=480, doc='Min storage (Wh)') model.price = Param(initialize = 0.000124, doc = 'Hourly Price of Electricity') #Decision Variables model.E = Var(model.T, domain=Reals) # Battery Power Flow model.S = Var(model.T, domain=NonNegativeReals) # Battery State of Charge model.excess_pv = Var(model.T, domain=Reals) # Excess PV Forecasted ## Set Constraints def storage_state(model,t): 'Storage changes with flows in/out' if t == model.T.first(): return model.S[t] == (model.Smax + model.Smin)/2 else: return model.S[t] == (model.S[t-1] - model.E[t-1]) model.charge_state = Constraint(model.T, rule = storage_state) def max_charge(model,t): 'Max hourly charge of the battery' return model.E[t] <= model.Ein model.pos_charge = Constraint(model.T, rule = max_charge) def max_discharge(model,t): 'Max hourly discharge of the battery' return model.E[t] >= model.Eout model.discharge = Constraint(model.T, rule = max_discharge) def max_stateofcharge(model,t): 'Max storage in the battery' return model.S[t] <= model.Smax model.max_soc = Constraint(model.T, rule = max_stateofcharge) def min_stateofcharge(model, t): 'Min storage in the battery' return model.S[t] >= model.Smin model.min_soc = Constraint(model.T, rule = min_stateofcharge) def stop_final_discharge(model, t): 'Prevents battery discharging in final hour' return model.E[t] <= model.S[t] model.final = Constraint(model.T, rule = stop_final_discharge) def excess_solar(model, t): 'Calculating the excess solar if the battery is full' if model.S[t] == model.Smax: return model.excess_pv[t] == demand[t] - solar_forecast[t] else: return model.excess_pv[t] == 0 model.solar_constraint = Constraint(model.T, rule = excess_solar) def demand_cons(model, t): 'Demand must be postive' if model.excess_pv[t] <= 0: return demand[t] - solar_forecast[t] - model.E[t] >= 0 else: return demand[t] + model.excess_pv[t] - solar_forecast[t] - model.E[t] >= 0 model.demand_constraint = Constraint(model.T, rule = demand_cons) # objective cost = sum(model.price*(demand[t] - solar_forecast[t] - model.E[t]) for t in model.T) model.cost = Objective(expr = cost, sense=minimize) # solve solvername='glpk' solverpath_folder='C:\\w64' solverpath_exe='C:\\w64\\glpsol' opt = SolverFactory(solvername,executable=solverpath_exe) results = opt.solve(model) print('Cost = €', round(value(model.cost()),2))

ERROR: evaluating object as numeric value: S[0] (object: <class 'pyomo.core.base.var._GeneralVarData'>) No value for uninitialized NumericValue object S[0] ERROR: Rule failed when generating expression for constraint solar_constraint with index 0: ValueError: No value for uninitialized NumericValue object S[0] ERROR: Constructing component 'solar_constraint' from data=None failed: ValueError: No value for uninitialized NumericValue object S[0] --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-4-cd4b3e07af68> in <module> 63 return Constraint.Skip 64 ---> 65 model.solar_constraint = Constraint(model.T, rule = excess_solar) 66 67 def demand_cons(model, t): c:\users\stephen\python\lib\site-packages\pyomo\core\base\block.py in __setattr__(self, name, val) 542 # Pyomo components are added with the add_component method. 543 # --> 544 self.add_component(name, val) 545 else: 546 # c:\users\stephen\python\lib\site-packages\pyomo\core\base\block.py in add_component(self, name, val) 1087 _blockName, str(data)) 1088 try: -> 1089 val.construct(data) 1090 except: 1091 err = sys.exc_info()[1] c:\users\stephen\python\lib\site-packages\pyomo\core\base\constraint.py in construct(self, data) 834 for index in self.index_set(): 835 self._setitem_when_not_present( --> 836 index, self.rule(block, index) 837 ) 838 except Exception: c:\users\stephen\python\lib\site-packages\pyomo\core\base\util.py in __call__(self, parent, idx) 302 return self._fcn(parent, *idx) 303 else: --> 304 return self._fcn(parent, idx) 305 306 <ipython-input-4-cd4b3e07af68> in excess_solar(model, t) 58 def excess_solar(model, t): 59 'Calculating the excess solar if the battery is full' ---> 60 if model.S[t] == model.Smax: 61 return model.excess_pv[t] == demand[t] - solar_forecast[t] 62 else: pyomo\core\expr\logical_expr.pyx in pyomo.core.expr.logical_expr.EqualityExpression.__nonzero__() pyomo\core\expr\numeric_expr.pyx in pyomo.core.expr.numeric_expr.ExpressionBase.__call__() c:\users\stephen\python\lib\site-packages\pyomo\core\expr\visitor.py in evaluate_expression(exp, exception, constant) 1052 visitor = _EvaluationVisitor() 1053 try: -> 1054 return visitor.dfs_postorder_stack(exp) 1055 1056 except ( TemplateExpressionError, ValueError, TypeError, c:\users\stephen\python\lib\site-packages\pyomo\core\expr\visitor.py in dfs_postorder_stack(self, node) 582 _sub = _argList[_idx] 583 _idx += 1 --> 584 flag, value = self.visiting_potential_leaf(_sub) 585 if flag: 586 _result.append( value ) c:\users\stephen\python\lib\site-packages\pyomo\core\expr\visitor.py in visiting_potential_leaf(self, node) 960 961 if node.is_numeric_type(): --> 962 return True, value(node) 963 elif node.is_logical_type(): 964 return True, value(node) pyomo\core\expr\numvalue.pyx in pyomo.core.expr.numvalue.value() pyomo\core\expr\numvalue.pyx in pyomo.core.expr.numvalue.value() ValueError: No value for uninitialized NumericValue object S[0]

1条回答

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1楼 · 发布于 2024-10-02 20:33:38

正如@Erwin所提到的，您不能在if语句中使用pyomo变量。因为在执行if语句时，变量的值是未知的。请参阅下面的代码注释，其中列出了有错误的行

def excess_solar(model, t):
    'Calculating the excess solar if the battery is full'
    if model.S[t] == model.Smax:  #here is the line with error
        return model.excess_pv[t] == demand[t] - solar_forecast[t]
    else: 
        return model.excess_pv[t] == 0

model.solar_constraint = Constraint(model.T, rule = excess_solar)

def demand_cons(model, t):
    'Demand must be postive'
    if model.excess_pv[t] <= 0: #error here too
        return demand[t] - solar_forecast[t] - model.E[t] >= 0
    else:
        return demand[t] + model.excess_pv[t] - solar_forecast[t] - model.E[t] >= 0 

model.demand_constraint = Constraint(model.T, rule = demand_cons)

我认为，如果你使用变量来确定约束是如何形成的，那么整个逻辑可能会有问题。我还没有讨论过您的示例，但我认为model.S应该是excess_solar约束的一部分，而不是用作if语句。过多的太阳能不会影响电池的最佳状态吗？类似的情况也可能发生在demand constraint上

对于excess solar约束，可能是这样的：

def excess_solar(model, t):
    'Calculating the excess solar if the battery is full'
     return model.excess_pv[t] + (model.S[t] - model.Smax) + (demand[t] - solar_forecast[t]) <= 0

如果要确保电池电量已满时model.excess_pv为0，可以在变量上设置一个界限，使其大于0。这只会导致约束松弛

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