As of February 2020, the PowerModelsMLD package has been deprecated and is not longer be maintained. Its functionality has been moved to the more general PowerModelsRestoration package. At the time of migration (i.e. PowerModels v0.14 and PowerModelsRestoration v0.1) migrating from PowerModelsMLD to PowerModelsRestoration simply requires updating the name of the package.
Dev:
A PowerModelsMLD provides extensions to PowerModels for solving the Maximum Load Delivery (MLD) problem. The MLD problem provides a reliable numerical method for solving challenging N-k damage scenarios, such as those that arise in the analysis of extreme events.
Core Problem Specifications
- Maximum Load Delivery with Discrete Variables (mld_uc)
- Maximum Load Delivery with Continuous Variables (mld)
Core Network Formulations
- AC (polar coordinates)
- DC Approximation (polar coordinates)
- SDP Relaxation (W-space)
- SOC Relaxation (W-space)
The primary entry point of the PowerModelsMLD package is the PowerModelsMLD.run_ac_mld_uc function, which provides a scalable heuristic for solving the AC-MLD problem.
The following example illustrates how to load a network, damage components and solve the AC-MLD problem.
using PowerModels; using PowerModelsMLD; using Ipopt
network_file = joinpath(dirname(pathof(PowerModels)), "../test/data/matpower/case5.m")
case = PowerModels.parse_file(network_file)
case["bus"]["2"]["bus_type"] = 4
case["gen"]["2"]["gen_status"] = 0
case["branch"]["7"]["br_status"] = 0
result = PowerModelsMLD.run_ac_mld_uc(case, with_optimizer(Ipopt.Optimizer))
The result data indicates that only 700 of the 1000 MWs can be delivered given the removal of bus 2, generator 2 and branch 7.
If you find PowerModelsMLD useful in your work, we kindly request that you cite the following publication:
@article{8494809,
author={Carleton Coffrin and Russel Bent and Byron Tasseff and Kaarthik Sundar and Scott Backhaus},
title={Relaxations of AC Maximal Load Delivery for Severe Contingency Analysis},
journal={IEEE Transactions on Power Systems},
volume={34}, number={2}, pages={1450-1458},
month={March}, year={2019},
doi={10.1109/TPWRS.2018.2876507}, ISSN={0885-8950}
}
Citation of the PowerModels framework is also encouraged when publishing works that use PowerModels extension packages.
This code is provided under a BSD license as part of the Multi-Infrastructure Control and Optimization Toolkit (MICOT) project, LA-CC-13-108.