Prof. Ray, Thank You. I'm sorry I completely forgot this basic thing. regards Mirish
On Thu, Feb 4, 2016 at 3:37 PM, Ray Zimmerman <[email protected]> wrote: > When the OPF does not converge successfully, the values contained in the > solution are meaningless. > > Ray > > > On Feb 4, 2016, at 9:30 AM, Mirish Thakur <[email protected]> wrote: > > Dear Prof.Ray, > > > I have observed result struct of load2disp whre I found some surprising > results on the buses where renewable generator and load both are > available. I kept renewable generation flexible in model so that it should > obey the line flow limits. I have used zero cost for renewable energy as > per my study requirement. I obsereved that there is negative dispatch of > renewable energy and dispatchable load is greater than actual connected > load. Please find herewith attched Excel file of result of two number of > such buses, bus No. 96 and 164. Where bus no 96 has both renewable > generator with 67.97 MW and connected load is 113.57+J 20.33. These are > some results when solution is not converged successfully. In some cases I > got perfect convergence. I really don't understand why this happens. I > really do appreciate your help. Thank you for your time. > > > Thanks and regards > > Mirish Thakur. > > On Wed, Dec 2, 2015 at 6:19 PM, Mirish Thakur <[email protected]> > wrote: > >> Dear Prof. Ray, >> >> Thanks a lot for clarifying my doubts. Actually I forgot to see that >> loads are modeled as negative generator, instead I was focusing on energy >> prices only. And in second one I didn't elaborate properly but I was >> thinking in the same way. Thank you very much for your time. >> >> Regards >> Mirish Thakur >> >> On Wed, Dec 2, 2015 at 6:02 PM, Ray Zimmerman <[email protected]> wrote: >> >>> 1) It looks like you are setting up gencost correctly. The negative >>> objective function value results from the fact that the curtailable loads >>> are being modeled as negative generation, with negative costs (positive >>> benefits). So rather than simply minimizing generating cost, the OPF is now >>> minimizing the negative of the net benefits (i.e. maximizing the net >>> benefits). The objective function value is then the negative of the net >>> benefits, so we expect it to be a large negative number. >>> >>> 2) Yes. But to be precise, these curtailments and redispatches are not >>> given directly by res.gen(:, PG), but by the deviation of res.gen(:, PG) >>> from >>> the corresponding nominal value, i.e. res.gen(:, PMAX) for renewables, >>> res.gen(:, >>> PMIN) for dispatchable loads, and the original dispatch reference point >>> for conventional generators. >>> >>> Ray >>> >>> >>> >>> On Dec 1, 2015, at 8:19 PM, Mirish Thakur <[email protected]> >>> wrote: >>> >>> Dear Prof. Ray Zimmerman, >>> >>> >>> Thank you very much for your help. I have implemented your suggestion of >>> relaxing constraints over conventional power plants and got successful >>> convergence on both model. While doing so in 2# model I kept minimum >>> value of generation Pmin=0, and instead of using single slack generator >>> output to supply system losses I increased all conventional generators >>> output by 2% means Pmax=(Original Pmax)*1.02. So that losses will be >>> contributed by all generators. But I want to clarify some doubts. >>> >>> >>> 1) My *Objective Function Value = -243875269.94 $/hr* is highly >>> negative after convergence, that I don’t understand why it’s highly >>> negative. After *mpc=load2disp(mycase)* I checked mpc.gencost matrix. >>> In my case conventional generators variable cost function is linear e.g. >>> [2 0 0 3 0 10.91 0] and renewable generators and cross border >>> generators cost is zero e.g. [2 0 0 3 0 0 0] and for dispatchable >>> loads I set linear cost function [2 0 0 3 0 5000 0]. Am I using >>> correct values in mpc.gencost matrix? Or I have to use for conventional >>> power plants = [2 0 0 3 0 0 0] and for both renewables and >>> dispatchable loads= [2 0 0 3 0 5000 0] ? Both approach gives negative >>> objective function value. >>> >>> >>> 2) When I perform successfully *res=runopf(mpc) *all curtailments on >>> renewable / loads / redispatch of conventional power plants will be seen >>> in *res.gen(:,2)* column (second column of res.gen matrix) right? Thank >>> you very much for your time. >>> >>> >>> Regards >>> >>> Mirish Thakur >>> >>> On Mon, Nov 30, 2015 at 5:54 PM, Ray Zimmerman <[email protected]> wrote: >>> >>>> Hi Mirish, >>>> >>>> There is no way to see the load or renewable curtailment until you get >>>> a converging OPF. It is possible that you will also have to redispatch the >>>> conventional generators in order to get a feasible solution. I would >>>> suggest that you combine your option #2 with curtailable loads, relax the >>>> active power dispatch constraints on the conventional generators (to their >>>> normal limits) and assign piecewise linear costs to those generators with >>>> negative or zero marginal cost up to Pg and large positive marginal cost >>>> above Pg. This will attempt to minimize deviations from the original >>>> dispatch pattern, hopefully moving only those generators necessary to >>>> relieve the line overloads. >>>> >>>> Ray >>>> >>>> On Nov 29, 2015, at 7:31 PM, Mirish Thakur <[email protected]> >>>> wrote: >>>> >>>> Hello friends, >>>> >>>> >>>> I’m working on (1000 bus system) reactive power dispatch problem. I >>>> have modeled grid into matpower case file and I’m getting the results of >>>> *runpf*. But when I use *ACOPF* it fails to converge. >>>> >>>> I have modeled grid into two methods >>>> >>>> 1) I used all renewable energy sources generation, pump storage power >>>> plant and cross border energy transfer as negative load. And all >>>> conventional power plants as generators. Dispatch of conventional >>>> generators is equal to residual load so demand is equal to generation. >>>> Further I have increased limits of slack generator to supply system losses >>>> and kept rest of generators dispatch constant by *Pmax=Pmin=Pg*. Also >>>> *RATE_A* limits should be unchanged. (Necessary condition for project). >>>> >>>> >>>> 2) In other way all renewable energy sources generation, pump storage >>>> power plant and cross border energy transfer are modeled as generators and >>>> put next to all conventional power plants. And in *gencost *matrix I >>>> used zero variable cost for renewable generation. Slack generator and rest >>>> of the conditions are set as it is in first approach. >>>> >>>> >>>> My question is in both modeling I got *runpf* successfully converged >>>> but I’m not getting convergence for *ACOPF*. So, I checked branch >>>> limits on some branches which I found overloaded by analyzing results of >>>> *res= >>>> runpf (mymodel)*. To avoid such overloading I want to change >>>> distribution pattern of load which might be cause of overloading of >>>> branches. I tried *load2disp* function to get curtailment on load but >>>> every time I got failure in convergence in *runopf*. I went through >>>> below mentioned discussions- >>>> >>>> https://www.mail-archive.com/matpower-l%40cornell.edu/msg04423.html >>>> >>>> https://www.mail-archive.com/matpower-l%40cornell.edu/msg00790.html >>>> >>>> https://www.mail-archive.com/matpower-l%40cornell.edu/msg01203.html >>>> >>>> Is there any way to see curtailment on load or negative generation >>>> (renewable generation/ cross border transfer of energy) so that I can >>>> redistribute that load /negative generation on other bus bars so that I >>>> can avoid overloading of branches and get successful convergence? Many >>>> thanks. >>>> >>>> Regards >>>> >>>> Mirish Thakur >>>> >>>> KIT University >>>> >>>> >>>> >>> >>> >> > <res.xlsx> > > >
