ANL-CEEESA/UnitCommitment.jl
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Alinson S. Xavier
2020-11-03 15:49:12 -06:00
commit b2480ef356
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# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
SHELL := /bin/bash
JULIA := julia --project=. --sysimage ../build/sysimage.so
TIMESTAMP := $(shell date "+%Y-%m-%d %H:%M")
SRC_FILES := $(wildcard ../src/*.jl)
INSTANCES_PGLIB := \
pglib-uc/ca/2014-09-01_reserves_0 \
pglib-uc/ca/2014-09-01_reserves_1 \
pglib-uc/ca/2014-09-01_reserves_3 \
pglib-uc/ca/2014-09-01_reserves_5 \
pglib-uc/ca/2014-12-01_reserves_0 \
pglib-uc/ca/2014-12-01_reserves_1 \
pglib-uc/ca/2014-12-01_reserves_3 \
pglib-uc/ca/2014-12-01_reserves_5 \
pglib-uc/ca/2015-03-01_reserves_0 \
pglib-uc/ca/2015-03-01_reserves_1 \
pglib-uc/ca/2015-03-01_reserves_3 \
pglib-uc/ca/2015-03-01_reserves_5 \
pglib-uc/ca/2015-06-01_reserves_0 \
pglib-uc/ca/2015-06-01_reserves_1 \
pglib-uc/ca/2015-06-01_reserves_3 \
pglib-uc/ca/2015-06-01_reserves_5 \
pglib-uc/ca/Scenario400_reserves_0 \
pglib-uc/ca/Scenario400_reserves_1 \
pglib-uc/ca/Scenario400_reserves_3 \
pglib-uc/ca/Scenario400_reserves_5 \
pglib-uc/ferc/2015-01-01_hw \
pglib-uc/ferc/2015-01-01_lw \
pglib-uc/ferc/2015-02-01_hw \
pglib-uc/ferc/2015-02-01_lw \
pglib-uc/ferc/2015-03-01_hw \
pglib-uc/ferc/2015-03-01_lw \
pglib-uc/ferc/2015-04-01_hw \
pglib-uc/ferc/2015-04-01_lw \
pglib-uc/ferc/2015-05-01_hw \
pglib-uc/ferc/2015-05-01_lw \
pglib-uc/ferc/2015-06-01_hw \
pglib-uc/ferc/2015-06-01_lw \
pglib-uc/ferc/2015-07-01_hw \
pglib-uc/ferc/2015-07-01_lw \
pglib-uc/ferc/2015-08-01_hw \
pglib-uc/ferc/2015-08-01_lw \
pglib-uc/ferc/2015-09-01_hw \
pglib-uc/ferc/2015-09-01_lw \
pglib-uc/ferc/2015-10-01_hw \
pglib-uc/ferc/2015-10-01_lw \
pglib-uc/ferc/2015-11-02_hw \
pglib-uc/ferc/2015-11-02_lw \
pglib-uc/ferc/2015-12-01_hw \
pglib-uc/ferc/2015-12-01_lw \
pglib-uc/rts_gmlc/2020-01-27 \
pglib-uc/rts_gmlc/2020-02-09 \
pglib-uc/rts_gmlc/2020-03-05 \
pglib-uc/rts_gmlc/2020-04-03 \
pglib-uc/rts_gmlc/2020-05-05 \
pglib-uc/rts_gmlc/2020-06-09 \
pglib-uc/rts_gmlc/2020-07-06 \
pglib-uc/rts_gmlc/2020-08-12 \
pglib-uc/rts_gmlc/2020-09-20 \
pglib-uc/rts_gmlc/2020-10-27 \
pglib-uc/rts_gmlc/2020-11-25 \
pglib-uc/rts_gmlc/2020-12-23
INSTANCES_MATPOWER := \
matpower/case118/2017-02-01 \
matpower/case118/2017-08-01 \
matpower/case300/2017-02-01 \
matpower/case300/2017-08-01 \
matpower/case1354pegase/2017-02-01 \
matpower/case1354pegase/2017-08-01 \
matpower/case1888rte/2017-02-01 \
matpower/case1888rte/2017-08-01 \
matpower/case1951rte/2017-02-01 \
matpower/case1951rte/2017-08-01 \
matpower/case2848rte/2017-02-01 \
matpower/case2848rte/2017-08-01 \
matpower/case2868rte/2017-02-01 \
matpower/case2868rte/2017-08-01 \
matpower/case3375wp/2017-02-01 \
matpower/case3375wp/2017-08-01 \
matpower/case6468rte/2017-02-01 \
matpower/case6468rte/2017-08-01 \
matpower/case6515rte/2017-02-01 \
matpower/case6515rte/2017-08-01
SAMPLES := 1 2 3
SOLUTIONS_MATPOWER := $(foreach s,$(SAMPLES),$(addprefix results/,$(addsuffix .$(s).sol.json,$(INSTANCES_MATPOWER))))
SOLUTIONS_PGLIB := $(foreach s,$(SAMPLES),$(addprefix results/,$(addsuffix .$(s).sol.json,$(INSTANCES_PGLIB))))
.PHONY: tables save small large clean-mps matpower pglib
all: matpower pglib
matpower: $(SOLUTIONS_MATPOWER)
pglib: $(SOLUTIONS_PGLIB)
clean:
@rm -rf tables/benchmark* tables/compare* results
clean-mps:
@rm -fv results/*/*/*.mps.gz
clean-sol:
@rm -rf results/*/*/*.sol.*
save:
mkdir -p "runs/$(TIMESTAMP)"
rsync -avP results tables "runs/$(TIMESTAMP)/"
results/%.sol.json: run.jl
@echo "run $*"
@mkdir -p $(dir results/$*)
@$(JULIA) run.jl $* 2>&1 | cat > results/$*.log
@echo "run $* [done]"
tables:
@mkdir -p tables
@python scripts/table.py
#@python scripts/compare.py tables/reference.csv tables/benchmark.csv

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uuid = "9e88b42a-f829-5b0c-bbe9-9e923198166b"
[[Sockets]]
uuid = "6462fe0b-24de-5631-8697-dd941f90decc"
[[SparseArrays]]
deps = ["LinearAlgebra", "Random"]
uuid = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
[[SpecialFunctions]]
deps = ["OpenSpecFun_jll"]
git-tree-sha1 = "d8d8b8a9f4119829410ecd706da4cc8594a1e020"
uuid = "276daf66-3868-5448-9aa4-cd146d93841b"
version = "0.10.3"
[[StaticArrays]]
deps = ["LinearAlgebra", "Random", "Statistics"]
git-tree-sha1 = "016d1e1a00fabc556473b07161da3d39726ded35"
uuid = "90137ffa-7385-5640-81b9-e52037218182"
version = "0.12.4"
[[Statistics]]
deps = ["LinearAlgebra", "SparseArrays"]
uuid = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
[[Test]]
deps = ["Distributed", "InteractiveUtils", "Logging", "Random"]
uuid = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
[[TimerOutputs]]
deps = ["Printf"]
git-tree-sha1 = "f458ca23ff80e46a630922c555d838303e4b9603"
uuid = "a759f4b9-e2f1-59dc-863e-4aeb61b1ea8f"
version = "0.5.6"
[[TranscodingStreams]]
deps = ["Random", "Test"]
git-tree-sha1 = "7c53c35547de1c5b9d46a4797cf6d8253807108c"
uuid = "3bb67fe8-82b1-5028-8e26-92a6c54297fa"
version = "0.9.5"
[[UUIDs]]
deps = ["Random", "SHA"]
uuid = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"
[[Unicode]]
uuid = "4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"
[[UnitCommitment]]
deps = ["Cbc", "DataStructures", "Documenter", "GLPK", "GZip", "JSON", "JuMP", "LinearAlgebra", "Logging", "MathOptFormat", "MathOptInterface", "PackageCompiler", "Printf", "Requires", "Revise", "SparseArrays", "Test", "TimerOutputs"]
path = ".."
uuid = "64606440-39ea-11e9-0f29-3303a1d3d877"
version = "2.1.0"
[[ZipFile]]
deps = ["Libdl", "Printf", "Zlib_jll"]
git-tree-sha1 = "254975fef2fc526583bb9b7c9420fe66ffe09f2f"
uuid = "a5390f91-8eb1-5f08-bee0-b1d1ffed6cea"
version = "0.9.2"
[[Zlib_jll]]
deps = ["Libdl", "Pkg"]
git-tree-sha1 = "622d8b6dc0c7e8029f17127703de9819134d1b71"
uuid = "83775a58-1f1d-513f-b197-d71354ab007a"
version = "1.2.11+14"

5
benchmark/Project.toml Normal file
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[deps]
Gurobi = "2e9cd046-0924-5485-92f1-d5272153d98b"
JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6"
JuMP = "4076af6c-e467-56ae-b986-b466b2749572"
UnitCommitment = "64606440-39ea-11e9-0f29-3303a1d3d877"

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benchmark/run.jl Normal file
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# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
using UnitCommitment
using JuMP
using Gurobi
using JSON
using Logging
using Printf
using LinearAlgebra
function main()
basename, suffix = split(ARGS[1], ".")
solution_filename = "results/$basename.$suffix.sol.json"
model_filename = "results/$basename.$suffix.mps.gz"
time_limit = 60 * 20
BLAS.set_num_threads(4)
global_logger(TimeLogger(initial_time = time()))
total_time = @elapsed begin
@info "Reading: $basename"
time_read = @elapsed begin
instance = UnitCommitment.read_benchmark(basename)
end
@info @sprintf("Read problem in %.2f seconds", time_read)
time_model = @elapsed begin
model = build_model(instance=instance,
optimizer=optimizer_with_attributes(Gurobi.Optimizer,
"Threads" => 4,
"Seed" => rand(1:1000),
))
end
@info "Optimizing..."
BLAS.set_num_threads(1)
UnitCommitment.optimize!(model, time_limit=time_limit, gap_limit=1e-3)
end
@info @sprintf("Total time was %.2f seconds", total_time)
@info "Writing: $solution_filename"
solution = UnitCommitment.get_solution(model)
open(solution_filename, "w") do file
JSON.print(file, solution, 2)
end
@info "Verifying solution..."
UnitCommitment.validate(instance, solution)
@info "Setting variable names..."
UnitCommitment.set_variable_names!(model)
@info "Exporting model..."
JuMP.write_to_file(model.mip, model_filename)
end
main()

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benchmark/scripts/compare.py Normal file
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# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import sys
easy_cutoff = 120
b1 = pd.read_csv(sys.argv[1], index_col=0)
b2 = pd.read_csv(sys.argv[2], index_col=0)
c1 = b1.groupby(["Group", "Instance", "Sample"])[["Optimization time (s)", "Primal bound"]].mean()
c2 = b2.groupby(["Group", "Instance", "Sample"])[["Optimization time (s)", "Primal bound"]].mean()
c1.columns = ["A Time (s)", "A Value"]
c2.columns = ["B Time (s)", "B Value"]
merged = pd.concat([c1, c2], axis=1)
merged["Speedup"] = merged["A Time (s)"] / merged["B Time (s)"]
merged["Time diff (s)"] = merged["B Time (s)"] - merged["A Time (s)"]
merged["Value diff (%)"] = np.round((merged["B Value"] - merged["A Value"]) / merged["A Value"] * 100.0, 5)
merged.loc[merged.loc[:, "B Time (s)"] <= 0, "Speedup"] = float("nan")
merged.loc[merged.loc[:, "B Time (s)"] <= 0, "Time diff (s)"] = float("nan")
merged = merged[(merged["A Time (s)"] >= easy_cutoff) | (merged["B Time (s)"] >= easy_cutoff)]
merged.reset_index(inplace=True)
merged["Name"] = merged["Group"] + "/" + merged["Instance"]
merged = merged.sort_values(by="Speedup", ascending=False)
k = len(merged.groupby("Name"))
plt.figure(figsize=(12, 0.50 * k))
plt.rcParams['xtick.bottom'] = plt.rcParams['xtick.labelbottom'] = True
plt.rcParams['xtick.top'] = plt.rcParams['xtick.labeltop'] = True
sns.set_style("whitegrid")
sns.set_palette("Set1")
sns.barplot(data=merged,
x="Speedup",
y="Name",
color="tab:red",
capsize=0.15,
errcolor="k",
errwidth=1.25)
plt.axvline(1.0, linestyle="--", color="k")
plt.tight_layout()
print("Writing tables/compare.png")
plt.savefig("tables/compare.png", dpi=150)
print("Writing tables/compare.csv")
merged.loc[:, ["Group",
"Instance",
"Sample",
"A Time (s)",
"B Time (s)",
"Speedup",
"Time diff (s)",
"A Value",
"B Value",
"Value diff (%)",
]
].to_csv("tables/compare.csv", index_label="Index")

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benchmark/scripts/table.py Normal file
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# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
from pathlib import Path
import pandas as pd
import re
from tabulate import tabulate
def process_all_log_files():
pathlist = Path(".").glob('results/*/*/*.log')
rows = []
for path in pathlist:
if ".ipy" in str(path):
continue
row = process(str(path))
rows += [row]
df = pd.DataFrame(rows)
df = df.sort_values(["Group", "Buses"])
df.index = range(len(df))
print("Writing tables/benchmark.csv")
df.to_csv("tables/benchmark.csv", index_label="Index")
def process(filename):
parts = filename.replace(".log", "").split("/")
group_name = "/".join(parts[1:-1])
instance_name = parts[-1]
instance_name, sample_name = instance_name.split(".")
nodes = 0.0
optimize_time = 0.0
simplex_iterations = 0.0
primal_bound = None
dual_bound = None
gap = None
root_obj = None
root_iterations = 0.0
root_time = 0.0
n_rows_orig, n_rows_presolved = None, None
n_cols_orig, n_cols_presolved = None, None
n_nz_orig, n_nz_presolved = None, None
n_cont_vars_presolved, n_bin_vars_presolved = None, None
read_time, model_time, isf_time, total_time = None, None, None, None
cb_calls, cb_time = 0, 0.0
transmission_count, transmission_time, transmission_calls = 0, 0.0, 0
# m = re.search("case([0-9]*)", instance_name)
# n_buses = int(m.group(1))
n_buses = 0
with open(filename) as file:
for line in file.readlines():
m = re.search(r"Explored ([0-9.e+]*) nodes \(([0-9.e+]*) simplex iterations\) in ([0-9.e+]*) seconds", line)
if m is not None:
nodes += int(m.group(1))
simplex_iterations += int(m.group(2))
optimize_time += float(m.group(3))
m = re.search(r"Best objective ([0-9.e+]*), best bound ([0-9.e+]*), gap ([0-9.e+]*)\%", line)
if m is not None:
primal_bound = float(m.group(1))
dual_bound = float(m.group(2))
gap = round(float(m.group(3)), 3)
m = re.search(r"Root relaxation: objective ([0-9.e+]*), ([0-9.e+]*) iterations, ([0-9.e+]*) seconds", line)
if m is not None:
root_obj = float(m.group(1))
root_iterations += int(m.group(2))
root_time += float(m.group(3))
m = re.search(r"Presolved: ([0-9.e+]*) rows, ([0-9.e+]*) columns, ([0-9.e+]*) nonzeros", line)
if m is not None:
n_rows_presolved = int(m.group(1))
n_cols_presolved = int(m.group(2))
n_nz_presolved = int(m.group(3))
m = re.search(r"Optimize a model with ([0-9.e+]*) rows, ([0-9.e+]*) columns and ([0-9.e+]*) nonzeros", line)
if m is not None:
n_rows_orig = int(m.group(1))
n_cols_orig = int(m.group(2))
n_nz_orig = int(m.group(3))
m = re.search(r"Variable types: ([0-9.e+]*) continuous, ([0-9.e+]*) integer \(([0-9.e+]*) binary\)", line)
if m is not None:
n_cont_vars_presolved = int(m.group(1))
n_bin_vars_presolved = int(m.group(3))
m = re.search(r"Read problem in ([0-9.e+]*) seconds", line)
if m is not None:
read_time = float(m.group(1))
m = re.search(r"Computed ISF in ([0-9.e+]*) seconds", line)
if m is not None:
isf_time = float(m.group(1))
m = re.search(r"Built model in ([0-9.e+]*) seconds", line)
if m is not None:
model_time = float(m.group(1))
m = re.search(r"Total time was ([0-9.e+]*) seconds", line)
if m is not None:
total_time = float(m.group(1))
m = re.search(r"User-callback calls ([0-9.e+]*), time in user-callback ([0-9.e+]*) sec", line)
if m is not None:
cb_calls = int(m.group(1))
cb_time = float(m.group(2))
m = re.search(r"Verified transmission limits in ([0-9.e+]*) sec", line)
if m is not None:
transmission_time += float(m.group(1))
transmission_calls += 1
m = re.search(r".*MW overflow", line)
if m is not None:
transmission_count += 1
return {
"Group": group_name,
"Instance": instance_name,
"Sample": sample_name,
"Optimization time (s)": optimize_time,
"Read instance time (s)": read_time,
"Model construction time (s)": model_time,
"ISF & LODF computation time (s)": isf_time,
"Total time (s)": total_time,
"User-callback time": cb_time,
"User-callback calls": cb_calls,
"Gap (%)": gap,
"B&B Nodes": nodes,
"Simplex iterations": simplex_iterations,
"Primal bound": primal_bound,
"Dual bound": dual_bound,
"Root relaxation iterations": root_iterations,
"Root relaxation time": root_time,
"Root relaxation value": root_obj,
"Rows": n_rows_orig,
"Cols": n_cols_orig,
"Nonzeros": n_nz_orig,
"Rows (presolved)": n_rows_presolved,
"Cols (presolved)": n_cols_presolved,
"Nonzeros (presolved)": n_nz_presolved,
"Bin vars (presolved)": n_bin_vars_presolved,
"Cont vars (presolved)": n_cont_vars_presolved,
"Buses": n_buses,
"Transmission screening constraints": transmission_count,
"Transmission screening time": transmission_time,
"Transmission screening calls": transmission_calls,
}
def generate_chart():
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
tables = []
files = ["tables/benchmark.csv"]
for f in files:
table = pd.read_csv(f, index_col=0)
table.loc[:, "Instance"] = table.loc[:,"Group"] + "/" + table.loc[:,"Instance"]
table.loc[:, "Filename"] = f
tables += [table]
benchmark = pd.concat(tables, sort=True)
benchmark = benchmark.sort_values(by="Instance")
k = len(benchmark.groupby("Instance"))
plt.figure(figsize=(12, 0.50 * k))
sns.set_style("whitegrid")
sns.set_palette("Set1")
sns.barplot(y="Instance",
x="Total time (s)",
color="tab:red",
capsize=0.15,
errcolor="k",
errwidth=1.25,
data=benchmark);
plt.tight_layout()
print("Writing tables/benchmark.png")
plt.savefig("tables/benchmark.png", dpi=150);
if __name__ == "__main__":
process_all_log_files()
generate_chart()