[Python] How to solve boolean logic with Pyomo

I need to include a calculation in Pyomo optimization but I receive a message that it becomes boolean logic. I am using glpk which is suitable for linear programming. Is there an alternative to include this calculation in the optimization? Below is a very simplified example.

spread_min_150 = 0.01
spread_150_max = 0.05

SPOT = 600
avg = 10
h = 730


def price(SPOT, POSITION, spread_min_150, spread_150_max):
if SPOT < 150:
spread_price = SPOT * (1 + spread_min_150) if POSITION >= 0 else SPOT * (1 - spread_min_150)
else:
spread_price = SPOT * (1 + spread_150_max) if POSITION >= 0 else SPOT * (1 - spread_150_max)
return spread_price

model = pyo.ConcreteModel()
model.a = pyo.Var(domain=pyo.NonNegativeReals)
model.b = pyo.Var(domain=pyo.NonNegativeReals)

mcp = avg * 730 - (model.a*730 + model.b*730)

B = mcp * price(SPOT, mcp, spread_min_150, spread_150_max)

model.obj = pybjective(expr=B, sense=pyo.minimize)

model.obj.pprint()

opt = pyo.SolverFactory('glpk') # ('glpk', executable='/usr/bin/glpsol')
result = opt.solve(model)
print(result)
model.display()


The error message is below.

---------------------------------------------------------------------------
PyomoException Traceback (most recent call last)
<ipython-input-5-c3d5ce23abb2> in <cell line: 22>()
20 mcp = avg * 730 - (model.a*730 + model.b*730)
21
---> 22 B = mcp * price(SPOT, mcp, spread_min_150, spread_150_max)
23
24 model.obj = pybjective(expr=B, sense=pyo.minimize)

1 frames
/usr/local/lib/python3.10/dist-packages/pyomo/core/expr/relational_expr.py in __bool__(self)
46 if self.is_constant():
47 return bool(self())
---> 48 raise PyomoException(
49 """
50 Cannot convert non-constant Pyomo expression (%s) to bool.

PyomoException: Cannot convert non-constant Pyomo expression (0 <= 7300 - (730*a + 730*b)) to bool.
This error is usually caused by using a Var, unit, or mutable Param in a
Boolean context such as an "if" statement, or when checking container
membership or equality. For example,
>>> m.x = Var()
>>> if m.x >= 1:
... pass
and
>>> m.y = Var()
>>> if m.y in [m.x, m.y]:
... pass
would both cause this exception.

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