Closed mzy2240 closed 1 year ago
Thanks for the bug report, @mzy2240. I am currently unable to reproduce it (on Linux). The error I get when solving your provided instance file is the one below. Does this instance have any disconnected buses or islands?
[ Info: Building model...
[ Info: Computing injection shift factors...
ERROR: LinearAlgebra.SingularException(2)
Stacktrace:
[1] checknonsingular
@ ~/Software/julia-1.9.0/share/julia/stdlib/v1.9/LinearAlgebra/src/factorization.jl:19 [inlined]
[2] checknonsingular
@ ~/Software/julia-1.9.0/share/julia/stdlib/v1.9/LinearAlgebra/src/factorization.jl:22 [inlined]
[3] #lu!#170
@ ~/Software/julia-1.9.0/share/julia/stdlib/v1.9/LinearAlgebra/src/lu.jl:82 [inlined]
[4] lu!
@ ~/Software/julia-1.9.0/share/julia/stdlib/v1.9/LinearAlgebra/src/lu.jl:80 [inlined]
[5] #lu#176
@ ~/Software/julia-1.9.0/share/julia/stdlib/v1.9/LinearAlgebra/src/lu.jl:299 [inlined]
[6] lu (repeats 2 times)
@ ~/Software/julia-1.9.0/share/julia/stdlib/v1.9/LinearAlgebra/src/lu.jl:298 [inlined]
[7] inv(A::Matrix{Float64})
@ LinearAlgebra ~/Software/julia-1.9.0/share/julia/stdlib/v1.9/LinearAlgebra/src/dense.jl:917
[8] _injection_shift_factors(; buses::Vector{UnitCommitment.Bus}, lines::Vector{UnitCommitment.TransmissionLine})
@ UnitCommitment ~/.julia/packages/UnitCommitment/G53EO/src/model/formulations/base/sensitivity.jl:23
have you applied this change? (use 1/x instead of b for susceptance)
function _susceptance_matrix(lines::Array{TransmissionLine})
return Diagonal([1.0/l.reactance for l in lines])
end
@mzy2240 Even with this change I can't reproduce it on Linux; there are no issues computing the matrix and the algorithm converges. I'll try to reproduce it later on macOS. Thanks for the report.
@mzy2240 Sorry, I forgot to ask. Could you please share the output of using Pkg; Pkg.status(mode=PKGMODE_MANIFEST)
so that we know what exact package versions are you using? Thanks!
Status `~/.julia/environments/v1.8/Manifest.toml`
[14f7f29c] AMD v0.5.0
⌃ [ac637c84] AbbreviatedStackTraces v0.1.10
⌃ [621f4979] AbstractFFTs v1.3.1
⌅ [1520ce14] AbstractTrees v0.3.4
[79e6a3ab] Adapt v3.6.2
[163e0771] Aegis v0.7.16 `~/Desktop/Aegis`
[28312eec] Alert v1.2.0
⌃ [07493b3f] Alpine v0.5.4
[7c4d4715] AmplNLWriter v1.1.0 `[https://github.com/jump-dev/AmplNLWriter.jl.git#master`](https://github.com/jump-dev/AmplNLWriter.jl.git#master%60)
[94bf388d] Apollo v1.3.19 `~/Desktop/Apollo`
⌅ [13e28ba4] AppleAccelerate v0.3.3
[ec485272] ArnoldiMethod v0.2.0
[4fba245c] ArrayInterface v7.4.11
[30b0a656] ArrayInterfaceCore v0.1.29
[dd5226c6] ArrayInterfaceStaticArraysCore v0.1.4
[65a8f2f4] ArraysOfArrays v0.6.1
[69666777] Arrow v2.6.2
[31f734f8] ArrowTypes v2.2.2
[fbb218c0] BSON v0.3.7
[f55c55da] BasicLU v0.2.1
[6e4b80f9] BenchmarkTools v1.3.2
[b99e7846] BinaryProvider v0.5.10
[d1d4a3ce] BitFlags v0.1.7
[c3b6d118] BitIntegers v0.3.0
[62783981] BitTwiddlingConvenienceFunctions v0.1.5
[163b9779] Blobs v0.5.1
⌃ [8e7c35d0] BlockArrays v0.11.2
[b3460a73] BlockSparseMatrices v0.1.0 `[https://github.com/KristofferC/BlockSparseMatrices.jl.git#master`](https://github.com/KristofferC/BlockSparseMatrices.jl.git#master%60)
[a74b3585] Blosc v0.7.3
[e1450e63] BufferedStreams v1.2.0
[fa961155] CEnum v0.4.2
⌃ [227a2e2d] COPT v1.1.1
[1e616198] COSMO v0.8.7
[bbd8fffe] COSMOAccelerators v0.1.0
[2a0fbf3d] CPUSummary v0.2.3
⌃ [336ed68f] CSV v0.10.9
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⌃ [9e997f8a] ChangesOfVariables v0.1.7
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⌃ [944b1d66] CodecZlib v0.7.1
[6b39b394] CodecZstd v0.7.2
[3da002f7] ColorTypes v0.11.4
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⌃ [f67ccb44] HDF5 v0.16.14
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[87dc4568] HiGHS v1.5.1 `[https://github.com/jump-dev/HiGHS.jl#master`](https://github.com/jump-dev/HiGHS.jl#master%60)
[3e5b6fbb] HostCPUFeatures v0.1.15
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⌃ [d05aeea4] LuxurySparse v0.7.1
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[3da0fdf6] MPIPreferences v0.1.8
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[4d1e1d77] Nullables v1.0.0
[ab2f91bb] OSQP v0.8.0
[510215fc] Observables v0.5.4
⌃ [6fd5a793] Octavian v0.3.22
⌃ [6fe1bfb0] OffsetArrays v1.12.9
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⌃ [438e738f] PyCall v1.95.1
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[bfc457fd] QDLDL v0.4.1
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⌅ [05181044] RelocatableFolders v0.1.3
[ae029012] Requires v1.3.0
⌃ [295af30f] Revise v3.5.2
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⌃ [06e1c1a7] TiledIteration v0.3.1
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⌃ [4f6342f7] Xorg_libX11_jll v1.6.9+4
⌃ [0c0b7dd1] Xorg_libXau_jll v1.0.9+4
[3c9796d7] Xorg_libXcomposite_jll v0.4.5+4
[935fb764] Xorg_libXcursor_jll v1.2.0+4
[0aeada51] Xorg_libXdamage_jll v1.1.5+4
⌃ [a3789734] Xorg_libXdmcp_jll v1.1.3+4
[1082639a] Xorg_libXext_jll v1.3.4+4
[d091e8ba] Xorg_libXfixes_jll v5.0.3+4
[a51aa0fd] Xorg_libXi_jll v1.7.10+4
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there are no issues computing the matrix and the algorithm converges.
You got the final solution and did not observe the infinite loop?
I also use a specified slack instead of picking the first bus as slack:
# Read buses
if haskey(json["Buses"][values(first(json["Buses"]))[1]], "Offset")
for (bus_name, dict) in json["Buses"]
bus = Bus(
bus_name,
Int64(dict["Offset"]),
timeseries(dict["Load (MW)"]),
ThermalUnit[],
PriceSensitiveLoad[],
ProfiledUnit[],
)
name_to_bus[bus_name] = bus
push!(buses, bus)
end
else
for (bus_name, dict) in json["Buses"]
bus = Bus(
bus_name,
length(buses),
timeseries(dict["Load (MW)"]),
ThermalUnit[],
PriceSensitiveLoad[],
ProfiledUnit[],
)
name_to_bus[bus_name] = bus
push!(buses, bus)
end
end
close in favor of https://github.com/ANL-CEEESA/UnitCommitment.jl/issues/41
Tested it on MacOS 12, Julia 1.9, but I'm still unable to reproduce the issue.
Hi @oyurdakul, do you think this is the same infinite loop you observed a while ago?
Caused by the custom bus offset
Irrelevant but do you have any idea how to model contingencies like loss of load and loss of gen?
Caused by the custom bus offset
OK; closing as invalid, then.
Irrelevant but do you have any idea how to model contingencies like loss of load and loss of gen?
The most straightforward (and least accurate) approach is just to set an adequate level of reserves. Another approach I'm familiar with, but which has not been implemented yet in the package, is a Benders-like algorithm in which the master problem finds the unit commitment decisions for the base case, and multiple independent subproblems check the feasibility of these decisions under different contingency scenarios. If one of the subproblems is infeasible, feasibility cuts are computed, added to the master problem, and the procedure repeats. See reference below. PRs implementing this method would be very welcome.
Description
Failed to solve a ED (single snapshot, no contingency) and optimization stuck in an infinite loop.
Steps to Reproduce
Rename the attached txt file to json, then load just like other instance json. You can use any MIP solver to solve (HiGHS, COPT, Gurobi, etc.) example.txt
(Update) Use 1/x instead of b as the susceptance. You can do that by modifying the
_susceptance_matrix
function:System Information