Jose, Would it be fine with you if the steps you’ve mentioned below are added to MATPOWER FAQ#5 http://www.pserc.cornell.edu//matpower/#pfconvergence Many a times, useful and detailed suggestions, such as what you’ve enumerated, get lost in email exchanges and someone trying to pull up this information has to resort to digging it out of the archive. It’ll be good to have your steps up on the FAQ.
Thanks, Shri From: Jose Luis Marin <mari...@gridquant.com<mailto:mari...@gridquant.com>> Reply-To: MATPOWER discussion forum <matpowe...@list.cornell.edu<mailto:matpowe...@list.cornell.edu>> Date: Wednesday, August 12, 2015 at 2:42 AM To: MATPOWER discussion forum <matpowe...@list.cornell.edu<mailto:matpowe...@list.cornell.edu>> Subject: Re: convergence problem in runpf. Mirish, I couldn't help notice that you're building this model from scratch (well, from a database) and you mentioned "To make the problem simple I used all buses as PQ buses except one slack bus". This actually makes it harder to converge, unless you have *very* accurate data on what the reactive injections Q (on generator buses) should be. May I suggest a different, incremental approach: 1. Start by keeping all generator buses you can as PV, instead of PQ. They will help holding up the voltage profile. After all, a PV node is a slack bus in what regards the reactive power injection. 2. For the loads, start by zeroing out PD (real power demand), but keeping QD (reactive demand) 3. For generators, set the scheduled PG to zero 4. For lines & transformers, zero out the resistance R 5. The resulting network will be a "purely reactive power" model. Now run a powerflow. If this doesn't have a feasible powerflow solution, it is because some branches have an X parameter that is too large (or equivalently, some load QD is too large). Ramp down the profile of QD until you see convergence. 6. Look at the resulting Q flows across branches, and try to detect anomalously large values (i.e. clear outliers). They will help you uncover values of X that may be wrong (too large). Also, keep an eye on negative X coming from equivalents such as 3-winding transformers; they may also be wrong. 7. Once you get that working, ramp up the values of PD on loads and PG on generators (keeping an eye on the swing's resulting PG, in order to redistribute big excesses). 8. Finally ramp up the resistance on lines. The whole idea is based on the fact that, for transmission networks (lines with R<<X), the reactive flows are like the "backbone" on which real power flows can sort of "ride on". Get a healthy backbone first, and then you can start transporting real power. Hope it helps, -- Jose L. Marin Gridquant España SL Grupo AIA On Wed, Aug 12, 2015 at 2:36 AM, Mirish Thakur <mirishtha...@gmail.com<mailto:mirishtha...@gmail.com>> wrote: Dear Mr.Shree, Thank you very much for your help. As per your suggestion and FAQ I tried to find out the problems. The results I got- 1) Fast-decoupled power flow did not converge in 30 iterations. 2) By following http://www.pserc.cornell.edu/matpower/#pfconvergence I tried to runcpf to get good initial guess and i got results like step 1 : lambda = 0.084, corrector did not converge in 10 iterations. Where lambda is < 1 and for reducing steady state loading limitation I reduced demand less than 60 % which also failed to converge the power flow. 3) Also I tried to run an optimal power flow according to Dr. Ray's explanation given in following link- https://www.mail-archive.com/search?l=matpower-l@cornell.edu&q=subject:%22Re%5C%3A+%5C%5BMatpower%5C%5D+3500+bus+simulation%22&o=newest but got the results like- MATPOWER Version 5.1, 20-Mar-2015 -- AC Optimal Power Flow MATLAB Interior Point Solver -- MIPS, Version 1.2, 20-Mar-2015 (using built-in linear solver) it objective step size feascond gradcond compcond costcond ---- ------------ --------- ------------ ------------ ------------ ------------ 0 1200199.7 2.41677 0.71 536.762 0 1 946197.39 15.531 1.3682 1.75871 525.914 0.209885 2 954529.91 15.405 0.766107 0.203773 297.341 0.00871422 3 954849.8 12.849 0.727712 0.0545952 258.471 0.00033166 4 954629.03 13035 0.69114 0.107402 258.048 0.000228815 5 954614.88 33406 0.692682 0.255673 257.828 1.46744e-05 6 954525.69 14111 0.579613 0.143897 256.765 9.24569e-05 7 954539.42 61648 0.581139 0.501345 255.994 1.42362e-05 8 954518.93 22452 0.573652 0.478609 255.465 2.12443e-05 9 954494.92 8540.4 0.556318 0.403754 254.653 2.48944e-05 10 954523.58 20366 0.556265 0.570707 254.104 2.97206e-05 11 954522.07 6142.4 0.554989 0.647881 256.561 1.57288e-06 12 954573.42 6192.9 0.513972 0.716706 253.604 5.32434e-05 13 954575.97 5912.1 0.509457 0.699751 252.612 2.64406e-06 14 954576.23 16534 0.509454 0.674865 253.278 2.64555e-07 15 954579.65 12324 0.509394 0.812237 252.966 3.54362e-06 16 954579.86 7650.3 0.509391 0.80973 252.948 2.18359e-07 17 954579.87 8185.1 0.509391 0.809591 252.947 1.48635e-08 18 954579.88 8696.2 0.509391 0.809411 252.945 1.31087e-08 19 954579.9 9392.5 0.50939 0.80927 252.943 1.3818e-08 Numerically Failed Did not converge in 19 iterations. >>>>> Did NOT converge (3.71 seconds) <<<<< 4) But when I used spy(J) , to look jacobian matrix it gives me some strange distribution. Herewith I attached image of jacobian matrix. ( I have modeled transmission lines and transformers to get one single branch matrix e.g. branch_matrix=vertcat(transmission_lines,grid_transformer) which is similar to matpower test cases.). So could you please suggest me what necessary steps I should follow? Thank you for your time. Regards Mirish Thakur KIT, University. On Mon, Aug 10, 2015 at 7:14 PM, Abhyankar, Shrirang G. <abhy...@anl.gov<mailto:abhy...@anl.gov>> wrote: I would suggest trying the following: 1. Use the solution of a fast decoupled power flow or an optimal power flow (with line limits and voltage limits relaxed) as the initial guess for the power flow. 2. Follow step 5 in http://www.pserc.cornell.edu/matpower/#pfconvergence making CPF to stop when the nose-point is reached. This can be done via results = runcpf(mpcbase,mpctarget,mpoption(‘cpf.stop_at’,’NOSE’)). If results.cpf.max_lam is >= 1, then it shows that the initial guess for the power flow is the problem for its divergence. To obtain a ‘good’ initial guess, run the continuation power flow again making it to stop exactly at lam = 1 (the target case loading and generation) via results = runcpf(mpcbase,mpctarget,mpoption(‘cpf.stop_at’,1.0)). You can then save the results struct as a matpower case file (via savecase()). On the other hand, if results.cpf.max_lam < 1, then the loading/generation in your original case is beyond the system steady-state loading limit. Shri From: Mirish Thakur <mirishtha...@gmail.com<mailto:mirishtha...@gmail.com>> Reply-To: MATPOWER discussion forum <matpowe...@list.cornell.edu<mailto:matpowe...@list.cornell.edu>> Date: Monday, August 10, 2015 at 10:44 AM To: MATPOWER discussion forum <matpowe...@list.cornell.edu<mailto:matpowe...@list.cornell.edu>> Subject: convergence problem in runpf. Dear Matpower Community, I’m working on power flow project and have used grid data from database. I have modelled all line parameters (R X B) in p.u. system, also same for transformers and kept generator output until it satisfies active and reactive power demand. For renewable generation, I specified as negative demand on respective buses. I checked all possibilities mentioned in FAQ (http://www.pserc.cornell.edu/matpower/#pfconvergence ) but couldn’t figure out problem. Also I checked (case_info) to see any island but got full system without island. To make the problem simple I used all buses as PQ buses except one slack bus. Also my casefile converges for rundcpf but fails to runpf and gives error like ‘Newton's method power flow did not converge in 10 iterations.’ Also I found that when I use following code- opt = mpoption('OUT_BUS', 0, 'OUT_BRANCH', 0, 'VERBOSE', 2); mpc = loadcase('casefile'); results =runpf(mpc,opt); may be it gives me divergence of PQ mismatch instead of convergence. MATPOWER Version 5.1, 20-Mar-2015 -- AC Power Flow (Newton) it max P & Q mismatch (p.u.) ---- --------------------------- 0 2.296e+01 1 1.729e+01 2 2.450e+03 3 2.352e+03 4 6.962e+06 5 1.740e+06 6 4.352e+05 7 1.753e+07 8 4.382e+06 9 3.322e+06 10 8.303e+05 Newton's method power flow did not converge in 10 iterations. >>>>> Did NOT converge (0.23 seconds) <<<<< results = version: '2' baseMVA: 100 bus: [1086x13 double] gen: [467x21 double] branch: [2145x17 double] order: [1x1 struct] et: 0.2320 success: 0 I will be very thankful for your help. Regards Mirish Thakur. KIT, University.
