Hi Carlos, 1. You need not use ex_transmat() at all. It was simply a convenience function used to create the transmat cell array of transmission probability matrices for each period. You can write your own function to create your own general cell array of matrices which can certainly vary by period.
2. From Table 5-3 in the MOST User’s Manual, you’ll see that the 3rd dimension of the values field of a profile corresponds to the indices specified by the rows field. In the example rows = 1 and the 3rd dimension of values is a singleton. If you set rows to a vector, say rows = [1; 3; 4], then values(:, :, 1), then values(:, :, 2) and then values(:, :, 3) would correspond to the values for wind units 1, 3, and 4 respectively. 3. I’m not sure I follow the question. In neither case are we optimizing explicit individual trajectories. We are optimizing the cost of a set of probability weighted scenarios (and transitions) with certain costs and constraints on the envelope of trajectories. 4. Not exactly. It is actually an approximation of a multi-stage problem, where the full multi-stage decision tree is approximated with a Markovian decision process. Reference [5]<https://ieeexplore.ieee.org/document/8438942> in the manual (also ref 5 at https://matpower.org/publications/) attempts to explain this. Hope this helps, Ray [5] A. J. Lamadrid, D. Munoz-Alvarez, C. E. Murillo-Sanchez, R. D. Zimmerman, H. D. Shin and R. J. Thomas, “Using the MATPOWER Optimal Scheduling Tool to Test Power System Operation Methodologies Under Uncertainty,” Sustainable Energy, IEEE Transactions on, vol. 10, no. 3, pp. 1280–1289, July 2019. DOI: 10.1109/TSTE.2018.2865454<https://doi.org/10.1109/TSTE.2018.2865454> On Jan 15, 2020, at 11:04 AM, Carlos Ferrandon Cervantes <[email protected]<mailto:[email protected]>> wrote: Hello everyone: I've been working with MOST for some time now; specifically with the unit commitment problem. I have been able to add some linearised constraints to it, but now I face the challenge of using the stochastic programming option in MOST. I've followed the example "most_ex7_suc" for these cases: Stochastic Unit Commitment - Individual Trajectories and Stochastic Unit Commitment - Full Transition Probabilities and my questions are the next ones: 1. I've realised that in the matrix "transmat", only the first column is full with the three scenarios' probabilities, but for the rest of the columns we have the identity matrix size 3x3 for each time step.Then the "scenario_probs" variable is computed/updated with the operation: mdi.tstep(t).TransMat * mdi.CostWeights(1, 1:mdi.idx.nj(t-1), t-1). My question is: Can these scenario probabilities change through time? I mean, that for every column we could have different scenario probabilities in "transmat". And if this was the case, what changes would be needed for the function "ex_transmat" to reflect these changes? 2. For the wind input in the example "most_ex7_suc" we have three different values of wind. I am assuming they belong to the three scenarios specified but only for that wind input. If we had different wind inputs from different buses, what would the arrangement of windprofiles.values(:,:,:) be? 3. How can we see the difference betwen the Individual Trajectories and Full Transition Probabilities case? I've understood from the manual that the system "stays" in one path in the case of Individual Trajectories, but for the Full Transition Probabilities how can we see the transition between the three scenarios' possible paths? 4. Can we that MOST is a Two-Stage model in Full Stochastic Programming then? Right now Im only working with the full system, i.e. no contingencies. As usual, thank you so much in advance, -- Carlos Ferrandon
