FILTER_RAMP_TRANSITIONS Creates a transition mask for ramp reserves. MD = FILTER_RAMP_TRANSITIONS(MD0, THRESHOLD) Creates a transition mask for ramping reserves, including only transitions with probabilities greater than or equal to a given THRESHOLD value, where the probability of the transition from j1 to j2 is taken to be the conditional probability in TransMat multiplied by the conditional probability of being in state j1, given that you've made it to period t.

- most_ex7_suc MOST_EX7_SUC Examples of secure and stochastic unit commitment problems.
- t_most_suc T_MOST_SUC Tests of stochastic unit commitment optimizations.

0001 function md = filter_ramp_transitions(md0, threshold) 0002 %FILTER_RAMP_TRANSITIONS Creates a transition mask for ramp reserves. 0003 % MD = FILTER_RAMP_TRANSITIONS(MD0, THRESHOLD) 0004 % 0005 % Creates a transition mask for ramping reserves, including only transitions 0006 % with probabilities greater than or equal to a given THRESHOLD value, 0007 % where the probability of the transition from j1 to j2 is taken to be the 0008 % conditional probability in TransMat multiplied by the conditional 0009 % probability of being in state j1, given that you've made it to period t. 0010 0011 % MOST 0012 % Copyright (c) 2012-2016, Power Systems Engineering Research Center (PSERC) 0013 % by Ray Zimmerman, PSERC Cornell 0014 % 0015 % This file is part of MOST. 0016 % Covered by the 3-clause BSD License (see LICENSE file for details). 0017 % See http://www.pserc.cornell.edu/matpower/ for more info. 0018 0019 %% dimensions 0020 md = md0; 0021 nt = length(md.tstep); 0022 0023 %% create mask 0024 for t = 1:nt 0025 if t == 1 0026 prob = md.tstep(t).TransMat; 0027 else 0028 prob = md.tstep(t).TransMat * prob; 0029 end 0030 md.tstep(t).TransMask = diag(prob) * md.tstep(t).TransMat >= threshold; 0031 end

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