Defining by nature "control" variables as "state" variable on the scenario tree

[bitanalui]

I am solving a Multistage Stochastic Optimization problem, for dynamic cascade reservoir optimization. To incorporate non-anticipativity, release (control variable) needs to be decided prior to the realization of inflows at t, that is nodes belong to period t-1 . It is suggested to treat release as state variable. Is this implementation correct?

@variable(subproblem, release[r=1:6] >= Cascade_system[r].release_min, SDDP.State, initial_value = Cascade_system[r].release_initial)

and then it appears in the following constraint:

@constraints(subproblem,begin storage_equa[r = 1:6], storage[r].out == storage[r].in + (inflow[r] + sum(release[j].inA[r,j] for j=1:r))1.9835 end)

release[j].in is the release at node that belongs to period t-1.

[odow]

Without the full details, hard to offer advice. But seems correct.

See https://odow.github.io/SDDP.jl/stable/guides/access_previous_variables/#Access-a-decision-from-N-stages-ago

One thing you need to be careful of is that stage t is feasible for any release that might be chosen in stage t-1.

[bitanalui]

Hello Oscar, Thank you, by full details do you mean the complete model? Here I am attaching a complete model with a small input file. With the balance constraint as formulated with release[j].in we face infeasibility issue. ControlvsStateVariable.zip

[odow]

Closing because it seems like @bitanalui and @bolbolim are the same people, and because the infeasibility is likely the same as #646.

Let's keep the discussion to a single post please.

[bitanalui]

Hi Oscar, we are not the same people, but we are trying to address and solve two issues of one model.

Thank you!