Hello MatPower community,
I want to analyze monetary consequences of reactive power dispatch on energy market which is already considering real power prices only. For this I have data of conventional power plants dispatch for every hour in whole year and respective variable cost of generation. I’ve active and reactive power demand for each hour as well. For this case I want to keep generator dispatch Pg=Pmin=Pmax (no change in active power generation) and Pd and Qd (real and reactive demand) as per given for whole year. Also I want to keep RATE_A value constant in opf. But I’m facing convergence problem in runopf. runopf doesn’t converge until and unless I make Rate_A value 1.5 times and some changes in Pmax and Pmin values at input side. Is there any alternate way to get convergence without making any changes in Pg, Pmax, Pmin and Rate_A value? (For example any changes in line parameters or something else). Thank you for your time. Regards Mirish Thakur KIT University. On Thu, Sep 17, 2015 at 9:27 PM, Ray Zimmerman <r...@cornell.edu> wrote: > Yes, thanks, Jose. I’ve added another item to FAQ #5 with links to your > posts. > > Ray > > > > On Aug 16, 2015, at 11:03 PM, Abhyankar, Shrirang G. <abhy...@anl.gov> > wrote: > > Thank you. > > On Aug 15, 2015, at 12:06 PM, "Jose Luis Marin" <mari...@gridquant.com> > wrote: > > Sure, of course I have no problem with that. > > Also, I realized I missed one detail: if there were any phase-shifters in > the network, I would also (initially) set their phase-shifts to zero. That > way you would obtain a truly "pure reactive" network. Then, when you work > your way ramping up real power, you would also want to ramp those > phase-shifts back to their original values as well. > > -- > Jose L. Marin > Gridquant España SL > Grupo AIA > > > On Fri, Aug 14, 2015 at 10:17 PM, Abhyankar, Shrirang G. <abhy...@anl.gov> > wrote: > >> 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> >> Reply-To: MATPOWER discussion forum <matpowe...@list.cornell.edu> >> Date: Wednesday, August 12, 2015 at 2:42 AM >> To: MATPOWER discussion forum <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> >> 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 >>> <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 >>> > 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> >>>> Reply-To: MATPOWER discussion forum <matpowe...@list.cornell.edu> >>>> Date: Monday, August 10, 2015 at 10:44 AM >>>> To: MATPOWER discussion forum <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. >>>> >>>> >>> >> > >
