# Where is the "rate\_a" in PowerModels.jl from

**URL:** <https://discourse.julialang.org/t/where-is-the-rate-a-in-powermodels-jl-from/129424>\
**Category:** Optimization (Mathematical)\
**Tags:** question, powermodels\
**Created:** [May 29, 2025, 12:29am UTC](https://discourse.julialang.org/t/where-is-the-rate-a-in-powermodels-jl-from/129424 "2025-05-29T00:29:43Z")\
**Posts on this page:** 9\
**Page:** 1

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**Author:** ![WalterMadelim](https://avatars.discourse-cdn.com/v4/letter/w/3e96dc/32.png) [@WalterMadelim](https://discourse.julialang.org/u/WalterMadelim)\
**Post date:** [May 29, 2025, 12:29am UTC](https://discourse.julialang.org/t/where-is-the-rate-a-in-powermodels-jl-from/129424/1 "2025-05-29T00:29:43Z")

</div>

The data provided by PowerModels.jl is inconsistent with my local raw Matpower file.  
The latter is all zero (seems to mean that there is no limit), but the former has a vector of numbers that I can’t tell where they come from. (Moreover, what is its unit (in MVA? or in p.u. (which is unlikely)))

 ![image](https://global.discourse-cdn.com/julialang/original/3X/3/1/319c12704c7be82e458038caa10ff41d8054f00a.png)

The code is

```julia
import PowerModels
R = let
    PowerModels.silence()
    t = PowerModels.make_basic_network(PowerModels.parse_file("data/case118.m"))
    A = PowerModels.calc_basic_incidence_matrix(t)
    B = size(A, 1)
    [t["branch"]["$b"]["rate_a"] for b in 1:B]
end

```

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<div class="post-metadata">

**Author:** ![odow](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/odow/32/28685_2.png) [@odow](https://discourse.julialang.org/u/odow)\
**Post date:** [May 29, 2025, 12:48am UTC](https://discourse.julialang.org/t/where-is-the-rate-a-in-powermodels-jl-from/129424/2 "2025-05-29T00:48:43Z")

</div>

`make_basic_network` calls `calc_thermal_limits!`:

> <https://github.com/lanl-ansi/PowerModels.jl/blob/be6af59202a6868b20a41214cb341b883d62e5f0/src/core/data_basic.jl#L68-L69>

> <https://github.com/lanl-ansi/PowerModels.jl/blob/be6af59202a6868b20a41214cb341b883d62e5f0/src/core/data.jl#L1073-L1123>

---

<div class="post-metadata">

**Author:** ![odow](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/odow/32/28685_2.png) [@odow](https://discourse.julialang.org/u/odow)\
**Post date:** [May 29, 2025, 12:50am UTC](https://discourse.julialang.org/t/where-is-the-rate-a-in-powermodels-jl-from/129424/3 "2025-05-29T00:50:34Z")

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If you just want to read the raw data, you probably want to use `parse_file` instead of `make_basic_network`:

```julia
t = PowerModels.parse_file("data/case118.m")

```

---

<div class="post-metadata">

**Author:** ![WalterMadelim](https://avatars.discourse-cdn.com/v4/letter/w/3e96dc/32.png) [@WalterMadelim](https://discourse.julialang.org/u/WalterMadelim)\
**Post date:** [May 29, 2025, 1:48am UTC](https://discourse.julialang.org/t/where-is-the-rate-a-in-powermodels-jl-from/129424/4 "2025-05-29T01:48:06Z")

</div>

So, it is not an honest rate\_a in physical sense, but rather an inference conditional on other related data (e.g. voltage, angles).  
(will this calculated data be useful in practice? I wonder.)

---

<div class="post-metadata">

**Author:** ![odow](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/odow/32/28685_2.png) [@odow](https://discourse.julialang.org/u/odow)\
**Post date:** [May 29, 2025, 2:09am UTC](https://discourse.julialang.org/t/where-is-the-rate-a-in-powermodels-jl-from/129424/5 "2025-05-29T02:09:05Z")

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See [Basic Data Utilities · PowerModels](https://lanl-ansi.github.io/PowerModels.jl/stable/basic-data-utilities/), in particular “all branches have explicit thermal limits” and “users requiring any of the features listed above for their analysis should use the non-basic PowerModels routines.”

PowerModels is designed around some very specific formulations. They’re not for everyone.

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**Author:** ![ccoffrin](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/ccoffrin/32/400_2.png) [@ccoffrin](https://discourse.julialang.org/u/ccoffrin)\
**Post date:** [May 29, 2025, 4:42am UTC](https://discourse.julialang.org/t/where-is-the-rate-a-in-powermodels-jl-from/129424/6 "2025-05-29T04:42:42Z")

</div>

> [@WalterMadelim](#):
>
> So, it is not an honest rate\_a in physical sense, but rather an inference conditional on other related data (e.g. voltage, angles).  
> (will this calculated data be useful in practice? I wonder.)

You are correct, in that the derived `rate_a` will never be a binding constraint in this model, but it is a valid upper bound on the amount of apparent power that can flow across the line. That still provides some useful information I think.

@oscar gives good advise. `make_basic_network` was intended primarily as a tool for teaching. If you don’t want the properties that it enforces then work with the data output by `parse_file`.

To answer your original question, “Where is the “rate\_a” in PowerModels.jl from” if you do not call,  
`PowerModels.silence()` then you should see some logging messages about how `rate_a` values were missing so they were replaced with different ones.

Side note - Are you familiar with [PGLib-OPF](https://github.com/power-grid-lib/pglib-opf) and [PGLib.jl](https://github.com/noahrhodes/PGLib.jl)? These datasets have been cleaned up, so you should not see as many warnings during data reading process.

---

<div class="post-metadata">

**Author:** ![WalterMadelim](https://avatars.discourse-cdn.com/v4/letter/w/3e96dc/32.png) [@WalterMadelim](https://discourse.julialang.org/u/WalterMadelim)\
**Post date:** [May 29, 2025, 5:38am UTC](https://discourse.julialang.org/t/where-is-the-rate-a-in-powermodels-jl-from/129424/7 "2025-05-29T05:38:44Z")

</div>

> [@ccoffrin](#):
>
> Are you familiar with [PGLib-OPF](https://github.com/power-grid-lib/pglib-opf) and [PGLib.jl](https://github.com/noahrhodes/PGLib.jl)?

I’ll try to learn them, thanks!

Update: I’ve worked on this network `pglib_opf_case118_ieee.m`

> **PowerFlowWithJuMP**
>
> ```julia
> import SparseArrays, PGLib; RawDict = PGLib.pglib("pglib_opf_case118_ieee.m");
> import JuMP, Gurobi; GRB_ENV = Gurobi.Env();
> import LinearAlgebra.dot as dot
> 
> function rectify_dir!(bft)
> for r in 1:size(bft, 1)
> if bft[r, 1] > bft[r, 2]
> bft[r, 1], bft[r, 2] = bft[r, 2], bft[r, 1]
> elseif bft[r, 1] == bft[r, 2]
> error("self-branch")
> end
> end
> end; function assert_is_connected(bft)
> B = size(bft, 1)
> pb = Vector(1:B) # primal bs
> sn = [1] # subnet
> for ite in 1:B # only du a full iteration as a conservative choice
> progress = false
> for (i, b) in enumerate(pb) # take out branch b
> if bft[b, 1] in sn
> on = bft[b, 2] # the other node
> on ∉ sn && push!(sn, on)
> elseif bft[b, 2] in sn
> on = bft[b, 1] # the other node
> on ∉ sn && push!(sn, on)
> else
> continue
> end
> popat!(pb, i); progress = true; break # fathom branch b
> end
> if progress == false
> error("ite = $ite. All the rest branches are not connected to the subnet being investigated. Check the current subnet")
> end
> 1:maximum(bft) ⊆ sn && return # The graph is proved to be connected
> end
> error("here shouldn't be reached")
> end; function bft_2_A(bft)
> B, N = size(bft, 1), maximum(bft)
> return SparseArrays.sparse([Vector(1:B); Vector(1:B)], vec(bft), [-ones(Int, B); ones(Int, B)], B, N)
> end; function adjnode(n)
> nv = Int[]
> for ci in findall(x -> x == n, bft)
> b, c = ci.I # row(= branch), col
> on = bft[b, 3 - c]
> on ∉ nv && push!(nv, on)
> end
> sort(nv)
> end; function get_b(n, m) # 🟠 use this after `rectify_dir!(bft)` had been done
> n > m && ((n, m) = (m, n))
> for r in 1:size(bft, 1)
> (bft[r, 1] == n && bft[r, 2] == m) && return r
> end
> error("no such branch exist")
> end;
> function bpf(b, Dvec = Dref, pvec = pg) # dispatch function of power flow on branch b
> b == 9 && return -pvec[findfirst(x -> x == 10, Gnode)::Int] # b8[9] = -3
> b == 7 && return bpf(9) # b8[7] = -1
> b == 94 && return bpf(93) # b8[94] = -1
> b == 127 && return -bpf(126) # b8[127] = -1
> b == 134 && return -pvec[findfirst(x -> x == 87, Gnode)::Int] # b8[134] = -3
> b == 176 && return -pvec[findfirst(x -> x == 111, Gnode)::Int] # b8[176] = -3
> b == 184 && return Dvec[findfirst(x -> x == 117, Dnode)::Int] # b8[184] = -2
> dup_bvec = [67, 76, 99, 86, 124, 139, 142]
> b ∈ dup_bvec && return false # b8[dup_bvec] .= 0; b8[dup_bvec .- 1] .= 2
> f = findfirst(x -> x == b, F2B)
> return pf[f] # the JuMP variable
> end
> B, N = 186, 118; # fixed for this network
> G = 54; # ✅ in case118, there is no such case that 2 generators connecting to the same bus
> D = 99; # demands are data
> bft = let # load a rectified bft
> bft = [RawDict["branch"]["$r"][c == 1 ? "f_bus" : "t_bus"] for r in 1:length(RawDict["branch"]), c in 1:2]; # raw bft
> assert_is_connected(bft);
> rectify_dir!(bft);
> bft
> end; A = bft_2_A(bft);
> Dnode = [RawDict["load"]["$d"]["load_bus"] for d in 1:D]; # function n2d(n) return findfirst(x -> x == n, Dnode)::Int end;
> Dref = [RawDict["load"]["$d"]["pd"] for d in 1:D]; # demand vector (std reference)
> Gnode = [RawDict["gen"]["$g"]["gen_bus"] for g in 1:G]; # function n2g(n) return findfirst(x -> x == n, Gnode)::Int end;
> Cgen = let
> Cgen = zeros(G)
> for g in 1:G
> v = RawDict["gen"]["$g"]["cost"]
> isempty(v) || (Cgen[g] = v[1] / 1000)
> end
> Cgen
> end;
> n8 = let # elements being
> # [1]: a bus with load only, and whose degree is larger than 2
> # [2]: a bus with gen only, and whose degree is larger than 2
> # [3]: a bus with both power injection and load
> # [-1]: a bus with load only, and whose degree is 1, we won't add KCL constr here
> # [-3]: a bus with gen only, and whose degree is 1, we won't add KCL constr here
> # [-2]: a bare bus whose degree is 2, we won't add KCL constr here
> # [0]: a bare joint bus
> n8 = zeros(Int8, N)
> n8[Dnode] .+= 1
> n8[Gnode] .+= 2
> n8[117] = -1
> n8[[10, 87, 111]] .= -3
> n8[[9, 63, 81]] .= -2
> n8
> end; b8 = let # Note this procedure is specific to case118, therefore executing once is sufficient
> # elements being
> # [2]: this line represents a 2-parallel branch, works as a normal line
> # [1]: a normal line
> # [0]: this line was banned (pf ≡ 0) due to parallel redundancy, we won't allocate pf variable here
> # [-1]: power flow of this line hinges on one of its adjacent line, we won't allocate pf variable here
> # [-2]: power flow of this line hinges on the corresponding pure load, we won't allocate pf variable here
> # [-3]: power flow of this line hinges on the corresponding pure gen, we won't allocate pf variable here
> b8 = ones(Int8, size(bft, 1))
> b8[9] = -3
> b8[7] = -1
> b8[94] = -1
> b8[127] = -1
> b8[134] = -3
> b8[176] = -3
> b8[184] = -2
> dup_bvec = [67, 76, 99, 86, 124, 139, 142]
> b8[dup_bvec] .= 0; b8[dup_bvec .- 1] .= 2
> b8
> end; F2B = findall(s -> s ∈ 1:2, b8); F = length(F2B);
> model = JuMP.Model(() -> Gurobi.Optimizer(GRB_ENV)); JuMP.@variable(model, pg[g = 1:G] >= 0);
> JuMP.@variable(model, pf[f = 1:F]);
> let # KCL constrs
> for n in findall(s -> s == 0, n8)
> bv, sgv = SparseArrays.findnz(view(A, :, n))
> JuMP.@constraint(model, dot(sgv, bpf.(bv)) == 0)
> end
> for n in findall(s -> s == 1, n8)
> bv, sgv = SparseArrays.findnz(view(A, :, n))
> JuMP.@constraint(model, dot(sgv, bpf.(bv)) == Dref[findfirst(x -> x == n, Dnode)::Int])
> end
> for n in findall(s -> s == 3, n8)
> bv, sgv = SparseArrays.findnz(view(A, :, n))
> JuMP.@constraint(model, dot(sgv, bpf.(bv)) + pg[findfirst(x -> x == n, Gnode)::Int] == Dref[findfirst(x -> x == n, Dnode)::Int])
> end
> for n in findall(s -> s == 2, n8)
> bv, sgv = SparseArrays.findnz(view(A, :, n))
> JuMP.@constraint(model, dot(sgv, bpf.(bv)) + pg[findfirst(x -> x == n, Gnode)::Int] == 0)
> end
> end
> JuMP.@objective(model, Min, dot(Cgen, pg))
> JuMP.optimize!(model); JuMP.assert_is_solved_and_feasible(model; allow_local = false);
> pg_val = JuMP.value.(pg) # output power of all generators
> bpf_val = @. JuMP.value(bpf(1:B)) # power flow on all branches
> 
> ```

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**Author:** ![WalterMadelim](https://avatars.discourse-cdn.com/v4/letter/w/3e96dc/32.png) [@WalterMadelim](https://discourse.julialang.org/u/WalterMadelim)\
**Post date:** [May 29, 2025, 9:02am UTC](https://discourse.julialang.org/t/where-is-the-rate-a-in-powermodels-jl-from/129424/8 "2025-05-29T09:02:05Z")

</div>

> - /api/\*.m - heavily loaded test cases (i.e. binding thermal limit constraints)

> - /sad/\*.m - small phase angle difference cases (i.e. binding phase angle difference constraints)

I can understand the first thermal limit, but not the other one.  
Why is small phase angle difference considered “binding”? (I thought large is binding?)  
What is meant by “phase angle difference constraint”?

---

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**Author:** ![WalterMadelim](https://avatars.discourse-cdn.com/v4/letter/w/3e96dc/32.png) [@WalterMadelim](https://discourse.julialang.org/u/WalterMadelim)\
**Post date:** [May 29, 2025, 11:52pm UTC](https://discourse.julialang.org/t/where-is-the-rate-a-in-powermodels-jl-from/129424/9 "2025-05-29T23:52:24Z")

</div>

What does the “removing” in these logging mean?  
I can still access them normally.

```julia
julia> import PGLib

julia> d = PGLib.pglib("pglib_opf_case118_ieee.m");
[info | PowerModels]: removing 3 cost terms from generator 32: Float64[]
# omit many lines
[info | PowerModels]: removing 1 cost terms from generator 30: [2575.8442, 0.0]
[info | PowerModels]: removing 3 cost terms from generator 3: Float64[]

julia> d["gen"]["30"]["cost"]
2-element Vector{Float64}:
 2575.8442
    0.0

```