Thanks for your reply I got itwaiting for the reply for the rest of my
questions
من: Ray Zimmerman <[email protected]>
إلى: MATPOWER discussion forum <[email protected]>
نسخة كربونية: Carlos Murillo-Sánchez <[email protected]>
تاريخ الإرسال: الخميس 19 أبريل، 2018 9:26 م
الموضوع: Re: Linear Time Varying Dynamical System in MOST
I’ll answer the last question about 𝛾 and defer to Carlos to address the
questions about the Linear Time Varying Dynamical System in MOST.
When solving a multiperiod stochastic problem in MOST, we assume that, for the
sake of the optimization, we are unable to adequately evaluate the impact of
decisions taken after low probability events (contingencies) occur. We optimize
in order to handle the contingency, but if it occurs we assume you will be
re-running your optimization with new information in order to get back on track.
So, in MOST, when considering contingencies, each contingency has a certain
probability of occurring. Let’s suppose we are dealing with a 3-period problem
with a single base case, and single contingency in each period, each with a
conditional probability of occurring of 1%. That is 𝜓0tjk = 0.01 for all t, j
and k.
This means that in period 1, the probability of a contingency is 1% and the
probability of being in the base case, and transitioning to period 2 is 99%. So
𝛾2 is 0.99. The conditional probability of a contingency happening in period 2
(given that one didn’t happen in period 1) is also 1%, meaning that the
unconditional probability of being in the contingency state in period 2 is
0.99*0.01. And the unconditional probability of being in the base state in
period 2 and transitioning to period 3, is 0.99^2, this is 𝛾3.
I hope this helps,
Ray
On Apr 12, 2018, at 6:10 AM, Shady Mamdouh <[email protected]> wrote:
Sent from Yahoo Mail on Android
On Tue, Apr 10, 2018 at 4:34, Shady Mamdouh <[email protected]>
wrote: ,Hi MATPOWER friends
-I want to understand the "Linear Time Varying Dynamical System in MOST" in
some depth as the only example on it in the manual, "t_most_w_ds.m", is not
explained neither in the MOST manual nor in MATLAB help.
-I understood that variables depend on power dispatches can be constrained
using this feature like emissions or water level of hydro units but an example
explaining this is appreciated or explanation of "t_most_w_ds.m" example.
-Also, can I know more examples of variables that can be used with this feature
?
-Finally, If the parameter named "Gamma ɣ" be deeply explained I would be
thankful.(Gamma ɣ :Probability of making it to period "t" without branching of
the central path in a contingency in periods 1.......... t-1.)
Thanks in advance
Shady M. Sadek
PhD Student, Ain Shams Univ., Cairo, Egypt.
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