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Start building benchmarking structure
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‎Snakefile‎

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from shutil import move
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import gdown
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from urllib.request import urlretrieve
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from pathlib import Path
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CATS_GITHUB_URL = "https://raw.githubusercontent.com/WISPO-POP/CATS-CaliforniaTestSystem/f260d8bd89e68997bf12d24e767475b2f2b88a77/GIS/"
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ENERGY_BENCHMARKS = Path("benchmarks/energy_model")
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PREPROCESS_DIR = ENERGY_BENCHMARKS / "data/preprocess"
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POSTPROCESS_DIR = ENERGY_BENCHMARKS / "data/postprocess"
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SCRIPTS_DIR = ENERGY_BENCHMARKS / "scripts"
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configfile: "benchmarks/config.yaml"
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def generate_all_runs():
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import itertools
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problem_size_pairs = []
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for problem, problem_data in config["problems"].items():
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if problem_data is None or "size" not in problem_data:
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problem_size_pairs.append((problem, "0"))
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else:
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for size in problem_data["size"]:
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problem_size_pairs.append((problem, size))
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runs = [
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(problem, size, library, solver)
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for (problem, size), library, solver in itertools.product(
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problem_size_pairs,
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config["libraries"],
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config["solvers"]
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)
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]
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return runs
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rule all:
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input:
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loads=POSTPROCESS_DIR / "loads.parquet",
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lines=POSTPROCESS_DIR / "lines_simplified.parquet",
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generators=POSTPROCESS_DIR / "generators.parquet",
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yearly_limit=POSTPROCESS_DIR / "yearly_limits.parquet",
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vcf=POSTPROCESS_DIR / "variable_capacity_factors.parquet",
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default_target: True
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rule fetch_load_data:
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"""Downloads the load data from the Google Drive folder hosted by the CATS project (https://drive.google.com/drive/folders/1Zo6ZeZ1OSjHCOWZybbTd6PgO4DQFs8_K)"""
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output:
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PREPROCESS_DIR / "CATS_loads.csv",
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run:
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gdown.download(id="1Sz8st7g4Us6oijy1UYMPUvkA1XeZlIr8", output=output[0])
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rule fetch_generation_data:
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"""Downloads the generation data from the Google Drive folder hosted by the CATS project (https://drive.google.com/drive/folders/1Zo6ZeZ1OSjHCOWZybbTd6PgO4DQFs8_K)"""
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output:
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PREPROCESS_DIR / "CATS_generation.csv",
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run:
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gdown.download(id="1CxLlcwAEUy-JvJQdAfVydJ1p9Ecot-4d", output=output[0])
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rule fetch_line_data:
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output:
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PREPROCESS_DIR / "CATS_lines.json",
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run:
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urlretrieve(CATS_GITHUB_URL + "CATS_lines.json", output[0])
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rule fetch_generator_data:
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output:
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PREPROCESS_DIR / "CATS_generators.csv",
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run:
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urlretrieve(CATS_GITHUB_URL + "CATS_gens.csv", output[0])
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rule process_load_data:
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"""Convert the load data to narrow format and keep only the active loads."""
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input:
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PREPROCESS_DIR / "CATS_loads.csv",
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output:
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POSTPROCESS_DIR / "loads.parquet",
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notebook:
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str(SCRIPTS_DIR / "process_load_data.py.ipynb")
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rule process_line_data:
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"""Convert from .json to .parquet and keep only relevant columns."""
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input:
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PREPROCESS_DIR / "CATS_lines.json",
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output:
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POSTPROCESS_DIR / "lines.parquet",
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notebook:
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str(SCRIPTS_DIR / "process_lines_json.py.ipynb")
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rule process_generator_data:
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"""Group the generators by type and bus."""
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input:
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PREPROCESS_DIR / "CATS_generators.csv",
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output:
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POSTPROCESS_DIR / "generators.parquet",
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notebook:
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str(SCRIPTS_DIR / "process_generator_data.py.ipynb")
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rule compute_capacity_factors:
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"""Use the hourly generation data to create capacity factors by fuel type."""
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input:
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gen_capacity=POSTPROCESS_DIR / "generators.parquet",
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gen_dispatch=PREPROCESS_DIR / "CATS_generation.csv",
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output:
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yearly_limit=POSTPROCESS_DIR / "yearly_limits.parquet",
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vcf=POSTPROCESS_DIR / "variable_capacity_factors.parquet",
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notebook:
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str(SCRIPTS_DIR / "compute_capacity_factors.py.ipynb")
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rule simplify_network:
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input:
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lines=POSTPROCESS_DIR / "lines.parquet",
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generators=POSTPROCESS_DIR / "generators.parquet",
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loads=POSTPROCESS_DIR / "loads.parquet",
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output:
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POSTPROCESS_DIR / "lines_simplified.parquet",
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script:
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SCRIPTS_DIR / "simplify_network.py"
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[f"benchmarks/{problem}/results/{library}_{solver}_{size}.tsv"
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for problem, size, library, solver in generate_all_runs()]
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rule run_benchmark:
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benchmark:
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repeat("benchmarks/{problem}/results/{library}_{solver}_{size}.tsv", config["repeat"])
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shell:
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"python benchmarks/run_benchmarks {wildcards.problem} --library {wildcards.library} --solver {wildcards.solver} --size {wildcards.size}"

‎benchmarks/__init__.py‎

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‎benchmarks/config.yaml‎

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repeat: 3
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problems:
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facility_problem:
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size:
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- 25
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- 50
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- 75
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- 100
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# energy_problem:
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solvers:
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- gurobi
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# - highs
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# - mosek
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libraries:
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- pyoframe
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# - pyomo
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# - cvxpy
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# - gurobipy
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# - jump
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# - linopy
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# - pyoptinterface

‎benchmarks/energy_model/Snakefile‎

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from shutil import move
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import gdown
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from urllib.request import urlretrieve
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from pathlib import Path
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CATS_GITHUB_URL = "https://raw.githubusercontent.com/WISPO-POP/CATS-CaliforniaTestSystem/f260d8bd89e68997bf12d24e767475b2f2b88a77/GIS/"
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ENERGY_BENCHMARKS = Path("benchmarks/energy_model")
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PREPROCESS_DIR = ENERGY_BENCHMARKS / "data/preprocess"
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POSTPROCESS_DIR = ENERGY_BENCHMARKS / "data/postprocess"
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SCRIPTS_DIR = ENERGY_BENCHMARKS / "scripts"
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rule all:
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input:
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loads=POSTPROCESS_DIR / "loads.parquet",
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lines=POSTPROCESS_DIR / "lines_simplified.parquet",
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generators=POSTPROCESS_DIR / "generators.parquet",
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yearly_limit=POSTPROCESS_DIR / "yearly_limits.parquet",
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vcf=POSTPROCESS_DIR / "variable_capacity_factors.parquet",
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rule fetch_load_data:
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"""Downloads the load data from the Google Drive folder hosted by the CATS project (https://drive.google.com/drive/folders/1Zo6ZeZ1OSjHCOWZybbTd6PgO4DQFs8_K)"""
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output:
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PREPROCESS_DIR / "CATS_loads.csv",
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run:
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gdown.download(id="1Sz8st7g4Us6oijy1UYMPUvkA1XeZlIr8", output=output[0])
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rule fetch_generation_data:
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"""Downloads the generation data from the Google Drive folder hosted by the CATS project (https://drive.google.com/drive/folders/1Zo6ZeZ1OSjHCOWZybbTd6PgO4DQFs8_K)"""
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output:
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PREPROCESS_DIR / "CATS_generation.csv",
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run:
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gdown.download(id="1CxLlcwAEUy-JvJQdAfVydJ1p9Ecot-4d", output=output[0])
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rule fetch_line_data:
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output:
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PREPROCESS_DIR / "CATS_lines.json",
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run:
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urlretrieve(CATS_GITHUB_URL + "CATS_lines.json", output[0])
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rule fetch_generator_data:
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output:
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PREPROCESS_DIR / "CATS_generators.csv",
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run:
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urlretrieve(CATS_GITHUB_URL + "CATS_gens.csv", output[0])
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rule process_load_data:
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"""Convert the load data to narrow format and keep only the active loads."""
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input:
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PREPROCESS_DIR / "CATS_loads.csv",
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output:
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POSTPROCESS_DIR / "loads.parquet",
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notebook:
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str(SCRIPTS_DIR / "process_load_data.py.ipynb")
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rule process_line_data:
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"""Convert from .json to .parquet and keep only relevant columns."""
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input:
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PREPROCESS_DIR / "CATS_lines.json",
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output:
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POSTPROCESS_DIR / "lines.parquet",
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notebook:
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str(SCRIPTS_DIR / "process_lines_json.py.ipynb")
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rule process_generator_data:
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"""Group the generators by type and bus."""
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input:
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PREPROCESS_DIR / "CATS_generators.csv",
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output:
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POSTPROCESS_DIR / "generators.parquet",
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notebook:
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str(SCRIPTS_DIR / "process_generator_data.py.ipynb")
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rule compute_capacity_factors:
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"""Use the hourly generation data to create capacity factors by fuel type."""
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input:
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gen_capacity=POSTPROCESS_DIR / "generators.parquet",
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gen_dispatch=PREPROCESS_DIR / "CATS_generation.csv",
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output:
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yearly_limit=POSTPROCESS_DIR / "yearly_limits.parquet",
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vcf=POSTPROCESS_DIR / "variable_capacity_factors.parquet",
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notebook:
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str(SCRIPTS_DIR / "compute_capacity_factors.py.ipynb")
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rule simplify_network:
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input:
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lines=POSTPROCESS_DIR / "lines.parquet",
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generators=POSTPROCESS_DIR / "generators.parquet",
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loads=POSTPROCESS_DIR / "loads.parquet",
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output:
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POSTPROCESS_DIR / "lines_simplified.parquet",
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script:
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SCRIPTS_DIR / "simplify_network.py"
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‎benchmarks/facility_problem/README.md‎

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‎benchmarks/facility_problem/__init__.py‎

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‎benchmarks/facility_problem/bm_cvxpy.py‎

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‎benchmarks/facility_problem/bm_gurobipy.py‎

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‎benchmarks/facility_problem/bm_jump.jl‎

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# Copyright (c) 2022: Miles Lubin and contributors
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#
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# Use of this source code is governed by an MIT-style license that can be found
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# in the LICENSE.md file or at https://opensource.org/licenses/MIT.
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# See https://github.com/jump-dev/JuMPPaperBenchmarks
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using JuMP
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import Gurobi
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function solve_facility(model, G, F)
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set_time_limit_sec(model, 0.0)
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set_optimizer_attribute(model, "Presolve", 0)
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@variables(model, begin
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0 <= y[1:F, 1:2] <= 1
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s[0:G, 0:G, 1:F] >= 0
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z[0:G, 0:G, 1:F], Bin
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r[0:G, 0:G, 1:F, 1:2]
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d
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end)
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@objective(model, Min, d)
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@constraint(model, [i in 0:G, j in 0:G], sum(z[i,j,f] for f in 1:F) == 1)
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# d is the maximum of distances between customers
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# and their facilities. The original constraint is # d >= ||x - y|| - M(1-z)
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# where M = 1 for our data. Because Gurobi/CPLEX can't take SOCPs directly,
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# we need to rewite as a set of constraints and auxiliary variables:
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# s = d + M(1 - z) >= 0
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# r = x - y
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# r'r <= s^2
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M = 2 * sqrt(2)
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for i in 0:G, j in 0:G, f in 1:F
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@constraints(model, begin
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s[i, j, f] == d + M * (1 - z[i, j, f])
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r[i, j, f, 1] == i / G - y[f, 1]
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r[i, j, f, 2] == j / G - y[f, 2]
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sum(r[i, j, f, k]^2 for k in 1:2) <= s[i, j, f]^2
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end)
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end
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optimize!(model)
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return model
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end
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function get_model(arg)
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if arg == "direct"
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return direct_model(Gurobi.Optimizer())
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else
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return Model(Gurobi.Optimizer)
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end
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end
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function main(io::IO, Ns = [25, 50, 75, 100])
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for type in ["direct", "default"]
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for n in Ns
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start = time()
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model = solve_facility(get_model(type), n, n)
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run_time = round(time() - start, digits=2)
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num_var = num_variables(model)
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println(io, "JuMP_$type,fac-$n,$num_var,$run_time")
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end
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end
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end
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open(joinpath(@__DIR__, "benchmarks.csv"), "a") do io
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main(io)
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end

‎benchmarks/facility_problem/bm_linopy.py‎

Whitespace-only changes.

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