PowerRAFT is a program for computing the optimal power restoration strategy in a post-disaster scenario. It features a web-based interface as well as a command line interface for running the experiments.
This software accompanies the paper "Field teams coordination for earthquake-damaged distribution system energization" (arxiv) which is published in the journal "Reliability Engineering & System Safety". Please cite this paper if you find our work valuable.
Click this link or the image below to watch:
This repository contains a submodule. Thus, it needs to be cloned with --recurse-submodules option:
git clone --recurse-submodules https://github.com/necrashter/PowerRAFTIf you have already cloned the repository normally, run the following commands in the cloned directory to initialize the submodules:
git submodule update --init --recursiveRust must be installed in your system.
cd server
# Debug mode
cargo run
# Release mode, much faster
cargo run --releaseIf no errors occur, you should be able to access the web interface at http://127.0.0.1:8000.
The command line interface of PowerRAFT is named dmscli which stands for Disaster Management System Command Line Interface.
It can be run as follows:
cd dmscli
cargo run --release -- <arguments to dmscli>The best way to learn about the available command line arguments is to run the help command. There are multiple subcommands, each with their own arguments.
cd dmscli
# Get help on available subcommands:
cargo run --release -- help
# Get more information about the run subcommand:
cargo run --release -- run --help
# Similarly for other subcommands:
cargo run --release -- solve --helpShort aliases for subcommands are also accepted for convenience, e.g., r instead of run.
In this section, the commands for running the experiments conducted in the paper are provided. The section numbers are given in each subheading.
All experiments must be run in dmscli directory:
cd dmscli# WSCC 9-bus with starting teams (9, 9, 9)
cargo run --release -- r --no-save ../experiments/opt.wscc.t-9-9-9.json
# WSCC 9-bus with starting teams (9, 9)
cargo run --release -- r --no-save ../experiments/opt.wscc.t-9-9.json
# 12-bus with only bus 1 connected to TG and starting teams (1, 1, 1)
cargo run --release -- r --no-save ../experiments/opt.12-bus.a.json
# 12-bus with only buses 1 and 10 connected to TG and starting teams (1, 1)
cargo run --release -- r --no-save ../experiments/opt.12-bus.b.json
# 17-bus system with teams starting at (1, 1)
cargo run --release -- r --no-save ../experiments/opt.midsize00.sov.json
cargo run --release -- r --no-save ../experiments/opt.midsize00.sow.json
cargo run --release -- r --no-save ../experiments/opt.midsize00.spov.json
cargo run --release -- r --no-save ../experiments/opt.midsize00.spow.jsonNote that the last experiment (the one with the 17-bus system) is composed of 4 separate experiment files.
The resulting .json files must be merged manually in order to reproduce Figure 6.
To merge .json files, concatenate them in a text editor and replace ] [ parts between them with , so that it becomes one JSON array.
cargo run --release -- r --no-save ../experiments/team.wscc.json cargo run --release -- r --no-save ../experiments/bus.midsize.base.json
cargo run --release -- r --no-save ../experiments/bus.midsize.d1.json
cargo run --release -- r --no-save ../experiments/bus.midsize.d2.jsonNote that bus.midsize.d1.json contains additional starting configurations that were omitted in the paper for brevity.
To reproduce the exact graphs from the paper, the additional results from bus.midsize.d1.json must be removed and the remaining ones must be merged with the results from bus.midsize.base.json and bus.midsize.d2.json.
cargo run --release -- r --no-save ../experiments/branch.midsize.json cargo run --release -- r --no-save ../experiments/tg.12-bus.json
# The following is an omitted experiment where the failure probabilities of
# all buses are set to 0.25 in the previous experiment.
cargo run --release -- r --no-save ../experiments/tg.12-bus.pf25.json Please bear in mind that you will need more than 16 GB of RAM (approximately 24 GB) for IEEE-37 Full and IEEE-123 Part B.
cargo run --release -- r --no-save ../experiments/ieee37.full.json
cargo run --release -- r --no-save ../experiments/ieee37.parted.json
cargo run --release -- r --no-save ../experiments/ieee123.parted.jsonIf an experiment is executed successfully, the corresponding .json file containing the results will be created in dmscli/results directory.
Since JSON files are human readable, these files can be inspected manually.
Besides, the Python script dmscli/plot.py is provided for creating plots from these JSON files.
numpy and matplotlib libraries must be installed in order to use this script.
plot.py receives one positional argument containing the result JSON file.
In addition to this, -p argument must be provided to specify the plot type. Available plot types:
t: Execution timem: Memoryv: Value functionep: Average energization probabilityac: Average expected cost per busa: Average time until energizationst: Number of transitions and statess: Number of states
The optional -n argument determines the name of each run in experiment.
By default, the name given in the experiment file is used, but for optimization experiments, it's more convenient to use the optimization name instead.
When -n opt is provided, each run is named according to the optimizations used.
The naming convention for optimizations is explained in the paper.
Example usage:
python3 plot.py -p t -n opt results/opt.wscc.t-9-9-9.json
# Figure 13
python3 plot.py -p ac results/tg.12-bus.json
# Figure 14
python3 plot.py -p st results/tg.12-bus.json The created plots are saved in the results directory. The script does not display the plots after running.
Run smaller tests that evaluate the core functionality:
cargo testRun the integration tests that take longer to execute:
cargo test -- --ignoredRun both kinds of tests:
cargo test -- --include-ignoredFor more information, please see cargo-test documentation.
Please cite our paper if you found this work valuable:
@article{ISIK2024110050,
title = {Field teams coordination for earthquake-damaged distribution system energization},
journal = {Reliability Engineering & System Safety},
volume = {245},
pages = {110050},
year = {2024},
issn = {0951-8320},
doi = {https://doi.org/10.1016/j.ress.2024.110050},
url = {https://www.sciencedirect.com/science/article/pii/S095183202400125X},
author = {İlker Işık and Ebru {Aydin Gol}},
keywords = {Decision support, Markov decision process, Stochastic systems, Power networks, Disaster management},
abstract = {The re-energization of electrical distribution systems in a post-disaster scenario is of grave importance as most modern infrastructure systems rely heavily on the presence of electricity. This paper introduces a method to coordinate the field teams for the optimal energization of an electrical distribution system after an earthquake-induced blackout. The proposed method utilizes a Markov Decision Process (MDP) to create an optimal energization strategy, which aims to minimize the expected time to energize each distribution system component. The travel duration of each team and the possible outcomes of the energization attempts are considered in the state transitions. The failure probabilities of the system components are computed using the fragility curves of structures and the Peak Ground Acceleration (PGA) values which are encoded to the MDP model via transition probabilities. Furthermore, the proposed solution offers several methods to determine the non-optimal actions during the construction of the MDP and eliminate them in order to improve the run-time performance without sacrificing the optimality of the solution.}
}