Use the Pyomo solver

The Pyomo solver interface lets you back monee with any solver that Pyomo supports — including Gurobi, HiGHS, GLPK, CBC, and CPLEX. Use it when you need a MILP or MIQCP back-end, for example to solve an AC optimal power flow with the MISOCP_NETWORK_FORMULATION.

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Prerequisites

Install Pyomo and at least one solver back-end:

HiGHS (recommended)

Open-source LP / MILP / MIQCP solver. Works out of the box on most platforms.

pip install highspy

or via conda:

conda install -c conda-forge highs

GLPK

Open-source LP / MILP solver.

conda install -c conda-forge glpk

Gurobi

Commercial solver with a free academic licence available at gurobi.com. Install the Python bindings after obtaining a licence:

pip install gurobipy

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Minimal example

The following snippet builds a small electricity grid and solves an AC optimal power flow using the MISOCP relaxation and HiGHS:

import monee.express as mx
from monee.model.formulation import MISOCP_NETWORK_FORMULATION
from monee.solver.pyo import PyomoSolver

# Build the network
net = mx.create_multi_energy_network()

bus_0 = mx.create_bus(net)
bus_1 = mx.create_bus(net)

mx.create_line(net, bus_0, bus_1, 100, r_ohm_per_m=7e-5, x_ohm_per_m=7e-5)
mx.create_ext_power_grid(net, bus_0)
mx.create_power_load(net, bus_1, p_mw=0.1, q_mvar=0.0)

# Switch to the MISOCP formulation
net.set_formulation(MISOCP_NETWORK_FORMULATION)

# Solve with Pyomo + HiGHS
result = PyomoSolver().solve(net, solver_name="highs")
print(result.objective_value)

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Passing an optimisation problem

Pass an OptimizationProblem the same way as with the GEKKO solver:

import monee.problem as mp
from monee.solver.pyo import PyomoSolver

problem = mp.OptimizationProblem()
problem.controllable_demands((
    "regulation",
    mp.AttributeParameter(
        min=lambda a, v: 0,
        max=lambda a, v: 1,
        val=lambda a, v: 1,
    ),
))

result = PyomoSolver().solve(
    net,
    optimization_problem=problem,
    solver_name="gurobi",
)

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Solver name reference

The solver_name argument is forwarded to pyomo.environ.SolverFactory:

Value	Solver	Notes
"highs"	HiGHS	Open-source LP / MILP / MIQCP. pip install highspy.
"gurobi"	Gurobi	Commercial, requires a valid licence.
"glpk"	GLPK	Open-source, LP / MILP only.
"cbc"	CBC	Open-source, LP / MILP.
"ipopt"	IPOPT	Open-source NLP. Requires a separate IPOPT binary.

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Formulation compatibility

Not every formulation is compatible with every solver back-end. The GEKKO-specific helpers if2, max2, and sign2 are not available in the Pyomo translation layer and will raise NotImplementedError if a formulation tries to use them.

Tip

For the Pyomo back-end, prefer:

• MISOCP_NETWORK_FORMULATION for electricity optimal power flow.

• Custom formulations written with standard Pyomo / monee expressions.

See Formulations for the full list of built-in formulations and a guide to writing custom ones.