API Reference

hydrothermaloperation(alldata::Array{Dict{Any,Any}}, params::Dict)

Create a hydrothermal power operation model containing the policygraph the system data and the planning parameters.

Required parameters are:

• alldata is a vector of dicts with information of the problem's stages.
• param is a dict containing solution parameters.

Read hydro description json file.

create_param(;stages::Int = 1,
    model_constructor_grid = DCPPowerModel,
    model_constructor_grid_backward = model_constructor_grid,
    model_constructor_grid_forward = model_constructor_grid_backward,
    post_method = PowerModels.build_opf,
    optimizer = GLPK.Optimizer,
    optimizer_backward = optimizer,
    optimizer_forward = optimizer_backward,
    setting = Dict("output" => Dict("branch_flows" => true,"duals" => true)),
    verbose = false,
    stage_hours = 1)

Create Parameters Dictionary.

Keywords are:

• stages::Int : Number of stages.
• modelconstructorgrid : Network formulation (Types from https://github.com/lanl-ansi/PowerModels.jl).
• optimizer : Optimizer factory (http://www.juliaopt.org/JuMP.jl/v0.19.0/solvers/).
• setting : PowerModels settings (https://github.com/lanl-ansi/PowerModels.jl/blob/e28644bf85232a5322adeeb847c0d18b7ff4f235/src/core/base.jl#L6-L34)) .
• verbose : Boolean to indicate information prints.
• stage_hours : Number of hours in each stage.

train(hydromodel::HydroPowerModel;kwargs...)

Train future cost function using SDDP.

Keyword arguments (same as SDDP.train): https://github.com/odow/SDDP.jl/blob/0490bea2c46787e1d4d63a5491ea0106c7fe70cf/src/algorithm.jl#L780-L827