Methods

REopt.run_mpc — Function

run_mpc(m::JuMP.AbstractModel, fp::String)

Solve the model predictive control problem using the MPCScenario defined in the JSON file stored at the file path fp.

Returns a Dict of results with keys matching those in the MPCScenario.

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run_mpc(m::JuMP.AbstractModel,  d::Dict)

Solve the model predictive control problem using the MPCScenario defined in the dict d.

Returns a Dict of results with keys matching those in the MPCScenario.

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run_mpc(m::JuMP.AbstractModel, p::MPCInputs)

Solve the model predictive control problem using the MPCInputs.

Returns a Dict of results with keys matching those in the MPCScenario.

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run_mpc(m::JuMP.AbstractModel, ps::AbstractVector{MPCInputs})

Solve the model predictive control problem using multiple MPCInputs.

Returns a Dict of results with keys matching those in the MPCScenario.

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REopt.build_mpc! — Function

build_mpc!(m::JuMP.AbstractModel, p::MPCInputs)

Add variables and constraints for model predictive control model. Similar to a REopt model but with any length of horizon (instead of one calendar year), and the DER sizes must be provided.

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build_mpc!(m::JuMP.AbstractModel, ps::AbstractVector{MPCInputs})

Add variables and constraints for model predictive control model of multiple nodes. Similar to a REopt model but with any length of horizon (instead of one calendar year), and the DER sizes must be provided.

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