alpha_min applies only to MIPS. -- Ray Zimmerman Senior Research Associate B30 Warren Hall, Cornell University, Ithaca, NY 14853 phone: (607) 255-9645
On Oct 29, 2013, at 12:23 PM, Santiago Torres <[email protected]> wrote: > Dears Victor, Ray > > I have a question. This alpha_min = 1e-8 is independent of the solver? or > this value only apply when you work with MIPS? If this value is involved in > all the solvers, Victor have you tried with other solvers? > > Regards, > > Santiago > > > 2013/10/28 Victor Hugo Hinojosa M. <[email protected]> > Dear Professor Zimmerman, > > I’d like to know about the numerically failed criterion used by MIPS > algorithm. I attach the code used in the mips.m file. > > > > alpha_min = 1e-8; %% OPT_AP_AD_MIN > > > > if any(isnan(x)) || alphap < alpha_min || alphad < alpha_min || ... > > gamma < eps || gamma > 1/eps > > if opt.verbose > > fprintf('\nNumerically Failed\n'); > > end > > eflag = -1; > > break; > > end > > > > > > The MIPS algorithm implemented in Matpower is considered to have failed when > the primal and dual variables (alphap and alphad) are lower than a critical > value. > > For example, in the Garver test system I obtained the attached configuration > (garver_no_CV.m), where MIPS algorithm numerically failed; Virtual generators > are modeled in bus 2, 4 and 5. You could simulate the configuration using the > code “mpopt=mpoption; mpopt=mpoption('OPF_ALG',200,'VERBOSE',3); > rundcopf(garver_no_CV,mpopt);”. > > You can see that the algorithm fails in 23 iterations, but if you analyze the > decision variables in the last iteration, you will note that the solution > point is very near the optimal solution. The algorithm fails because the > condition used by MIPS (alphap < alpha_min || alphad < alpha_min), so I > realize that the algorithm needs more iterations to solve the DC-OPF problem. > Considering a sensitivity analysis, I use another alphan_min values (1e-9 and > 1e-10), and I run the DC-OPF problem again. The problem converges to the > optimal solution. > > I’d like to know about failed criterion used by MIPS algorithm in order to > understand the criterion and give some opinions about it. I have the > following questions: 1) Is the condition necessary?; and 2) What was the > criterion to formulate this condition? > > I accomplished a sensitivity analysis, and my recommendation will be increase > the critical value to 1e-10. I solved the static transmission planning > problem with this value using the SRA evolutionary algorithm, and I cannot > find any infeasible solution (numerically filed) conducting 100 simulations. > > I think the DC-OPF problem modeled by linear objective function and linear > constraints must find the optimal solution using the MIPS algorithm. > > I’ll wait for your comments and opinions about the analysis accomplished. > > Regards, > > Vh > > > > De: [email protected] > [mailto:[email protected]] En nombre de Victor Hugo > Hinojosa M. > Enviado el: lunes, 23 de septiembre de 2013 11:09 > Para: 'MATPOWER discussion forum' > Asunto: RE: Reasons for non convergence of optimal power flows > > > > Dear Professor Zimmerman, > > I’d like to study in more detail the problem of the initial point, so I want > to keep on working in this issue in order to obtain a better criterion. In > this moment, my single recommendation will be that user should consider a > different point. > > I think that the PTDF factors can help to improve the convergence of the MIPS > algorithm, so I need more time to develop some criterions. > > Regards, > > Víctor > > > > De: [email protected] > [mailto:[email protected]] En nombre de Ray Zimmerman > Enviado el: lunes, 16 de septiembre de 2013 13:35 > Para: MATPOWER discussion forum > Asunto: Re: Reasons for non convergence of optimal power flows > Importancia: Alta > > > > Thanks Victor for your input. The initial point used by MATPOWER for the MIPS > solver was simply an attempt to begin at some interior point, so I'm not > surprised at all to hear that selecting a different starting point can > sometimes result in convergence of case that ran into numerical problems. > > > > What wasn't clear to me was whether your tests suggest a way of selecting a > starting point that is likely to be consistently better than the one > currently being used. Or is it just an issue of – when one doesn't work, try > a different one? > > > > -- > > Ray Zimmerman > > Senior Research Associate > > B30 Warren Hall, Cornell University, Ithaca, NY 14853 > > phone: (607) 255-9645 > > > > > > > > > > On Sep 13, 2013, at 4:21 PM, Victor Hugo Hinojosa M. <[email protected]> > wrote: > > > > Dear Professor Zimmerman and Santiago, > > I had the same problem that Santiago mentioned when I applied an evolutionary > algorithm to the static and dynamic transmission expansion planning problem > (TEP). The algorithm was based on local random search, so many configurations > from the solution space were analyzed. I realized that some configuration > didn’t converge the Matpower, and I was trying to find out about this problem. > > For example, in the Garver test system I applied the evolutionary algorithm > to the static problem, and I obtained the attached configuration where MIPS > algorithm numerically failed. In the same way, I considered virtual > generators (bus 2, 4 and 5). > > The MIPS algorithm don’t solve the DC-OPF problem for this configuration > “mpopt=mpoption; mpopt=mpoption('OPF_ALG',200,'VERBOSE',3); > rundcopf(garver_no_CV,mpopt);”. > > This problem occurs due to the initial point that the MIPS algorithm use. In > the MIPS solver, the initial point is obtained considering the average power > between the minimal and maximal power for each generator. When I changed the > initial point to the minimal power generation (x0=[0 0 0 0 0 0]), the MIPS > algorithm converges. > > I had conducted some proofs in order to determine some initial points where > the algorithm has convergence problems. I’ve divided each generator range, so > the MIPS algorithm can consider different initial points. I included three > analysis. > > In first case, I divided the power range in 8 intervals for each generator, > so I can combine the power for each generator as initial point. The total > points that the algorithm must consider is 531 441 (9^6). For these points, > the MIPS algorithm doesn’t converge in 15 078 times (2.84%). In the second, I > divided in 9 intervals, so the total points is 10^6. In this case, the MIPS > algorithm doesn’t converge in 14 492 times (1.45%). Finally, I divided in 10 > intervals, so the total points is 11^6. In this case, the MIPS algorithm > doesn’t converge in 42 016 times (2.37%). I attached an excel file where it’s > possible to figure out the initial points that the MIPS algorithm doesn’t > converge considering 4 intervals. In the row 339, it’s possible to see the > initial point used by Matpower. > > I had the same problem when I used the MIPS algorithm considering the power > generator as decision variable. The solution could be to consider another > initial point, but I’d like to study again the problem. > > I hope your comments and ideas about the analysis carried out. > > Regards, > > Víctor > > > > > > De: [email protected] > [mailto:[email protected]] En nombre de Ray Zimmerman > Enviado el: martes, 28 de mayo de 2013 11:34 > Para: MATPOWER discussion forum > Asunto: Re: Reasons for non convergence of optimal power flows > > > > Islands should not be a problem as long as there is a REF bus in the island > and the available generation is sufficient to meet the load in each island. > So (1) is a definite possibility, but (2) shouldn't be an issue. Insufficient > reactive power range to keep voltage magnitudes within range, and overly > restrictive branch flow limits could be other causes of an infeasible OPF > problem. Aside from things that can cause the problem to actually be > infeasible, there are also numerical issues that can affect feasible > problems. These can be the result of large ranges in parameters (branch > impedances, generator costs, etc.). In these cases, often a different solver > or algorithm may be able to solve the problem successfully. > > > > Hope this helps, > > > > -- > > Ray Zimmerman > > Senior Research Associate > > 419A Warren Hall, Cornell University, Ithaca, NY 14853 > > phone: (607) 255-9645 > > > > > > > > > > On May 20, 2013, at 1:21 PM, Santiago Torres <[email protected]> wrote: > > > > > > Dear Ray, I my resarch work I am using many transmission topologies and also > I am using ficticious generators in order to get optimal power convergence > for those different transmission topologies. Using those artificial or > ficticious generators in all exclusive load buses is suposed to help for > convergence, however in practice I am getting some transmission > configurations that do not achieve convergence. > > > > I am thinking in the following reasons: > > > > 1) Too strict power generation limits of ficticious generators. > > > > 2) Some topologies with islanded nodes. > > > > Can you think in other reasons? > > > > Islanded nodes is a non convergence cause in Matpower? > > > > Best Regards, > > > > Santiago > > > -- > > Dr.-Ing. Santiago Torres > IEEE Senior Member > > Post-Doctoral Fellow > School of Electrical and Computer Engineering > > > > > University of Campinas, Campinas, SP, Brazil > > http://www.dsee.fee.unicamp.br/ > > Albert Einstein, 400 > 13083-852, Campinas, SP, Brazil > > > > <garver_no_CV.m><No_CV_analysis.xlsx> > > > > > > > -- > Dr.-Ing. Santiago Torres > IEEE Senior Member > > Power Systems Researcher
