Examples

To use REopt you will need to have a solver installed. REopt.jl has been tested with Xpress, Cbc, and CPLEX solvers, but it should work with other Linear Progam solvers (for PV and Storage scenarios) or Mixed Integer Linear Program solvers (for scenarios with outages and/or Generators).

Basic

using REopt, JuMP, Cbc

m = Model(Cbc.Optimizer)
results = run_reopt(m, "test/scenarios/pv_storage.json")

See pv_storage.json for details on the Scenario.

For more on the scenario.json see the REopt Inputs section.

In order to calculate additional result metrics you can run the BAUScenario scenario in parallel by providing two JuMP.Models like so:

m1 = Model(Cbc.Optimizer)
m2 = Model(Cbc.Optimizer)
results = run_reopt([m1,m2], "./scenarios/pv_storage.json")

With the BAUScenario results we can calculate the net present value of the optimal system.

Advanced

Modifying the mathematical model

Using the build_reopt! method and JuMP methods one can modify the REopt model before optimizing. In the following example we add a cost for curtailed PV power.

using Xpress
using JuMP
using REopt

m = JuMP.Model(Xpress.Optimizer)

p = REoptInputs("scenarios/pv_storage.json");

build_reopt!(m, p)

#= 
replace the original objective, which is to Min the Costs,
with the Costs + 100 * (total curtailed PV power)
=#  
JuMP.@objective(m, Min, m[:Costs] + 100 * sum(m[:dvCurtail]["PV", ts] for ts in p.time_steps));

JuMP.optimize!(m)  # normally this command is called in run_reopt

results = reopt_results(m, p)

One can also add variables and constraints to the model before optimizing using the JuMP commands.