Quick Start Guide

Once Petroleum Models is installed, Ipopt is installed, and a network data file (e.g. "test/data/case5.m") has been acquired, a Petroleum Flow can be executed with,

using PetroleumModels
using Ipopt
using JuMP

ipopt_solver = JuMP.optimizer_with_attributes(
    Ipopt.Optimizer,
    "print_level" => 5,
    "tol" => 1e-10,
)

PetroleumModels.run_opf("test/data/case5.m", LPPetroleumModel, ipopt_solver)

Getting Results

The run commands in PetroleumModels return detailed results data in the form of a dictionary. This dictionary can be saved for further processing as follows,

result = PetroleumModels.run_opf("test/data/case5.m", LPPetroleumModel, ipopt_solver)

For example, the algorithm's runtime, final objective value, and status can be accessed with,

result["solve_time"]
result["objective"]
result["termination_status"]

The "solution" field contains detailed information about the solution produced by the run method. For example, the following dictionary comprehension can be used to inspect the junction pressures in the solution,

Dict(name => data["h"] for (name, data) in result["solution"]["junction"])

For more information about PetroleumModels result data see the PetroleumModels Result Data Format section.

Accessing Different Formulations

The generic run_opf() allows one to solve a petroleum optimal flow problem with any petroleum network formulation implemented in PetroleumModels. For example, the full non convex Petroleum Flow can be run with,

run_opf("test/data/case5.m", LPPetroleumModel, ipopt_solver)

Modifying Network Data

The following example demonstrates one way to perform multiple PetroleumModels solves while modify the network data in Julia,

network_data = PetroleumModels.parse_file("test/data/case5.m")

run_opf(network_data, LPPetroleumModel, ipopt_solver)

network_data["junction"]["24"]["head_min"] = 0.0

run_opf(network_data, LPPetroleumModel, ipopt_solver)

For additional details about the network data, see the PetroleumModels Network Data Format section.

Inspecting the Formulation

The following example demonstrates how to break a run_opf call into separate model building and solving steps. This allows inspection of the JuMP model created by PetroleumModels for the petroleum flow problem,

pm = instantiate_model("test/data/case5.m", LPPetroleumModel, PetroleumModels.build_opf)
print(pm.model)
JuMP.set_optimizer(pm.model, ipopt_solver)
JuMP.optimize!(pm.model, ipopt_solver)

Solution conversion

The default behavior of PetroleumModels produces solution results in non-dimensionalized units. To recover solutions in SI units, the following function can be used

PetroleumModels.make_si_units!(result["solution"])