Iman: two methods are compared in this paper. The 2-point estimate
tries to capture moments of the distribution using just two
_deterministic_ OPFs. The cumulant method assumes linear propagation
(i.e., based on a sensitivity analysis of the KKT conditions) of the
pdfs of the quantities deemed random. This assumes that the projected
restrictions continue to fulfill some power flow. In other words, the
projected distributions fulfill linearized power flow deviations about a
base case solution, but not the true nonlinear power flow.
The nonlinear power flow is a deterministic problem. It does not make
sense to gather physical insight from problems posed in terms of
fulfillment of nonlinear constraints in an expected sense.
I think that we are still stuck with having to deal with distributions
through collections of samples, for the nonlinear power flow case.
iman wrote:
Dear All,
I came across this paper *"Probabilistic Optimal Power Flow
Applications to Electricity Markets" *by Gregor , Antony Schellenberg
and, William Rosehart
showing that probabilistic optimal power flow is done in Matpower.
Does anybody know how you can implement it in Matpower?
Probablistic optimal power flow is used to deal with variant nature of
generators such as wind.
Thanks
Iman
