how i can defined different bounds or fixed values to elements of a vector variable?
For example:
l = [0,1,2]
u = [1,2,3]
@variable(m, l[i] <= x[i=1:3] <= u[i])
thanks for your answer,but, in this case Julia warning!@variable(PF, P[i=1:nGen; bus[:,2][i] == 2] == gen[:,2][i]/baseMVA)
@variable(PF, P[i=1:nBus; bus[:,2][i] == 1] == -bus[:,3][i]/baseMVA)
JuMP has been eliminated predefined variable values.
You’ll need to provide more information (minimal stand-alone code example, full output, expected behavior).
You put code in triple backticks (`) to make it easier to follow (or use Ctrl+Shift+C, or the preformatted text button at the top).
using JuMP
PF = Model()
#system MVA
basebaseMVA = 100;
# bus data
# bus_i type Pd Qd Gs Bs area Vm Va baseKV zone Vmax Vmin
bus = [
1 3 0 0 0 0 1 1 0 345 1 1.1 0.9
2 2 0 0 0 0 1 1 0 345 1 1.1 0.9
3 2 0 0 0 0 1 1 0 345 1 1.1 0.9
4 1 0 0 0 0 1 1 0 345 1 1.1 0.9
5 1 90 30 0 0 1 1 0 345 1 1.1 0.9
6 1 0 0 0 0 1 1 0 345 1 1.1 0.9
7 1 100 35 0 0 1 1 0 345 1 1.1 0.9
8 1 0 0 0 0 1 1 0 345 1 1.1 0.9
9 1 125 50 0 0 1 1 0 345 1 1.1 0.9];
# generator data
# bus Pg Qg Qmax Qmin Vg mBase status Pmax Pmin Pc1 Pc2 Qc1min Qc1max Qc2min Qc2max ramp_agc ramp_10 ramp_30 ramp_q apf
gen = [
1 0 0 300 -300 1 100 1 250 10 0 0 0 0 0 0 0 0 0 0 0
2 163 0 300 -300 1 100 1 300 10 0 0 0 0 0 0 0 0 0 0 0
3 85 0 300 -300 1 100 1 270 10 0 0 0 0 0 0 0 0 0 0 0;];
# branch data# fbus tbus r x b rateA rateB rateC ratio angle status angmin angmax
branch = [
1 4 0 0.0576 0 250 250 250 0 0 1 -360 360
4 5 0.017 0.092 0.158 250 250 250 0 0 1 -360 360
5 6 0.039 0.17 0.358 150 150 150 0 0 1 -360 360
3 6 0 0.0586 0 300 300 300 0 0 1 -360 360
6 7 0.0119 0.1008 0.209 150 150 150 0 0 1 -360 360
7 8 0.0085 0.072 0.149 250 250 250 0 0 1 -360 360
8 2 0 0.0625 0 250 250 250 0 0 1 -360 360
8 9 0.032 0.161 0.306 250 250 250 0 0 1 -360 360
9 4 0.01 0.085 0.176 250 250 250 0 0 1 -360 360];
nBus=size(bus,1); # Number of Busses
nGen=size(gen,1); # Number of Generators
@variables PF begin
V[i=1:nBus], (lowerbound = bus[:,13][i], start = bus[:,8][i], upperbound = bus[:,12][i])
δ[i=1:nBus], (lowerbound = -pi, start = bus[:,9][i], upperbound = pi)
P[i=1:nBus]
Q[i=1:nBus]
end
@variable(PF, V[i=1:nBus;bus[:,2][i] == 3] == gen[:,6][i])
@variable(PF, δ[i=1:nBus;bus[:,2][i] == 3] == 0)
@variable(PF, gen[:,10][i]/baseMVA <= P[i=1:nBus;bus[:,2][i] == 3] <= gen[:,9][i]/baseMVA)
@variable(PF, gen[:,5][i]/baseMVA <= Q[i=1:nBus;bus[:,2][i] == 3] <= gen[:,4][i]/baseMVA)
@variable(PF, V[i=1:nBus;bus[:,2][i] == 2] == gen[:,6][i])
@variable(PF, P[i=1:nBus;bus[:,2][i] == 2] == gen[:,2][i]/baseMVA)
@variable(PF, gen[:,5][i]/baseMVA <= Q[i=1:nBus;bus[:,2][i] == 2] <= gen[:,4[i]/baseMVA)
@variable(PF, P[i=1:nBus;bus[:,2][i] == 1] == -bus[:,3][i]/baseMVA)
@variable(PF, Q[i=1:nBus;bus[:,2][i] == 1] == -bus[:,4][i]/baseMVA)
@variable(PF, bus[:,13][i] <= V[i=1:nBus;bus[:,2][i] == 1] <= bus[:,12][i])
NOTE: edited for formatting by @StefanKarpinski
For the last 10 lines, you should use @constraint instead of @variable
I did this, but the solution was infeasible.
This is surprising. Could you give the code that gave you the infeasible solution ?
If you primary interest is to solve AC Power Flows you might want to have a look at PowerModels.jl as an example.
thanks for your response, but i wanted to solve own AC power flow to continue that to reach the solution of the “State Estimation” problem
I am not an expert in power flow optimization but we solved a power flow problem using Convex.jl here. It would be interesting to know if Convex.jl can be used to satisfy your needs?
We also support optimization with complex variables and coefficients.
Just to clarify this point. The referenced Convex.jl model solves the SDP relaxation of the AC Power Flow equations. In some cases the SDP model can find a globally optimal solution to the AC model, but this is not guaranteed. Also the scalability of SDP solvers is a significant challenge for power system applications.
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