Dear Zimmerman, I got your point now that it represents states. However, in MOST manual, its written "If identity matrices are used for these transition probabilities, this results in the special case in which there are 3 full trajectories through the horizon, each of which can be viewed as a different scenario". I want to get transition probability for multiple wind farms with given output data (Joint PDF can be calculated). I want to ask that how correlation matrix and transition probability matrix are related. What about joint PDF or copula in case need to find dependence? My task is simple. I need to simulate 118 Bus system for multi-period Stochastic UC while simulating different wind farms states or scenarios (dependent scenarios will be preferred). Can you guide how i can do this simple task and another dependent scenarios task in MOST? Thanks for your help.
Regards, Aamir Nawaz On Tue, Sep 18, 2018 at 2:37 AM, Ray Zimmerman <[email protected]> wrote: > Yes. To illustrate with an extremely basic example, suppose you have two > wind farms and you can represent each with two state (high or low wind > output). You could create 4 states (scenarios) with the 4 combinations and > assign them reasonable probabilities in each period and compute the > corresponding transition probability matrices. Obviously, for larger > systems and for more than two output levels per generator, you will have to > use a much more sophisticated method for selecting your scenarios, > otherwise the problem explodes in size. > > Ray > > > On Sep 17, 2018, at 1:29 PM, Engineer Aamir Nawaz <[email protected]> > wrote: > > Dear Zimmerman, > Thanks for your kind response. As i understand, you mean that i should > find spatial correlation/joint distribution of all wind farms and put them > in transition matrix for each time interval. Please correct if i am wrong. > > Regards, > Aamir Nawaz > > On Tue, Sep 18, 2018 at 1:35 AM, Ray Zimmerman <[email protected]> wrote: > >> Well, I confess I don’t follow the intentions of your code, but I think >> you may have some misunderstandings of what we mean by a “scenario” in >> MOST. Think of it as a possible “state” of the system at a particular time >> (not a set of trajectories through time). So to model wind, you will want >> to attempt to model the joint distribution of all of your wind locations as >> a set of probability weighted “scenarios” or states for that moment in time. >> >> Ray >> >> >> On Sep 14, 2018, at 8:52 AM, Aamir Nawaz <[email protected]> wrote: >> >> Dear all, >> I want to find expected dispatch for IEEE 188 Bus system with 30% Wind >> farms where wind scenrios is nj=400 and for time period nt=12 hours. Please >> help where this code have problem. >> >> define_constants >> mpopt = mpoption('verbose', 0); >> mpc = loadcase(case118); >> nWF=fix(sum(mpc.gen(:,9))*0.3/100);%30% wind farms >> PVbus=find(mpc.bus(:,BUS_TYPE)~=1); >> xgd = loadxgendata(ex_xgd118, mpc); >> for i=1:nWF >> [iwind(i), mpc, xgd] = addwind(ex_wind_dynmc118, mpc,xgd); >> end >> nt =12;nj=400; >> weibullpdf=wblpdf(0.01:0.01:nj*0.01,1:nj,1); >> transmat = {repmat(weibullpdf,nt,1)}; >> profiles = getprofiles(uniformwindprofile(nj, nt), iwind); >> profiles = getprofiles('ex_load_profile118', profiles); >> mdi = loadmd(mpc, transmat, xgd, [], [], profiles); >> mpopt = mpoption('verbose', 0, 'most.dc_model', 1); >> mdo = most(mdi, mpopt); >> EPg = mdo.results.ExpectedDispatch >> >> But its giving expected dispatch for 1 hour only, instead of 12 hours. >> Can you point out my mistake? >> >> >> >> >> >> *Regards,Aamir Nawaz* >> >> >> > > >
