no code implementations • 18 Apr 2024 • Ryan Piansky, Georgia Stinchfield, Alyssa Kody, Daniel K. Molzahn, Jean-Paul Watson
Extending traditional progressive hedging techniques, we consider coupling in both placement variables across all scenarios and state-of-charge variables at temporal boundaries.
no code implementations • 15 Apr 2024 • Paprapee Buason, Sidhant Misra, Daniel K. Molzahn
The power flow equations are central to many problems in power system planning, analysis, and control.
no code implementations • 8 Apr 2024 • Babak Taheri, Rahul K. Gupta, Daniel K. Molzahn
Using sensitivity information, our algorithm optimizes the LinDistFlow approximation's coefficient and bias parameters to minimize discrepancies in predictions of voltage magnitudes relative to the nonlinear DistFlow model.
no code implementations • 30 Mar 2024 • Rahul K. Gupta, Daniel K. Molzahn
In this work, we combine these two schemes and provide extensive analyses and comparisons of these two fairness schemes.
no code implementations • 12 Mar 2024 • Rahul K. Gupta, Daniel K. Molzahn
However, such schemes result in increased overall curtailments.
no code implementations • 15 Jan 2024 • Rahul K. Gupta, Paprapee Buason, Daniel K. Molzahn
These limits are computed a day ahead of real-time operations by solving an offline stochastic optimization problem using forecasted scenarios for PV generation and load demand.
no code implementations • 22 Nov 2023 • Babak Taheri, Daniel K. Molzahn
This paper presents an algorithm to optimize the parameters of power systems equivalents to enhance the accuracy of the DC power flow approximation in reduced networks.
no code implementations • 14 Nov 2023 • Rachel Harris, Mohannad Alkhraijah, Daniel K. Molzahn
The future power grid may rely on distributed optimization to determine the set-points for huge numbers of distributed energy resources.
no code implementations • 20 Oct 2023 • Mohannad Alkhraijah, Rachel Harris, Samuel Litchfield, David Huggins, Daniel K. Molzahn
We evaluate the detection conditions' performance on three data manipulation strategies we previously proposed: simple, feedback, and bilevel optimization attacks.
no code implementations • 30 Sep 2023 • Babak Taheri, Daniel K. Molzahn
Inspired by techniques for training machine learning models, this paper proposes an algorithm that seeks optimal coefficient and bias parameters to improve the DC power flow approximation's accuracy.
no code implementations • 15 Sep 2023 • Ali Jalilian, Babak Taheri, Daniel K. Molzahn
This study introduces a mixed-integer linear programming (MILP) model, effectively co-optimizing patrolling, damage assessment, fault isolation, repair, and load re-energization processes.
1 code implementation • 22 May 2023 • Gustav Nilsson, Alejandro D. Owen Aquino, Samuel Coogan, Daniel K. Molzahn
Since both the transportation network and the power grid already experience periods of significant stress, joint analyses of both infrastructures will most likely be necessary to ensure acceptable operation in the future.
no code implementations • 22 Apr 2023 • Babak Taheri, Daniel K. Molzahn
By automatically learning the trustworthiness of various outputs from simplified OPF problems, these parameters inform the online computations of the state estimation-based algorithm to both recover feasible solutions and characterize the performance of power flow approximations, relaxations, and ML models.
1 code implementation • 2 Apr 2023 • Mohannad Alkhraijah, Rachel Harris, Carleton Coffrin, Daniel K. Molzahn
This paper presents PowerModelsADA, an open-source framework for solving Optimal Power Flow (OPF) problems using Alternating Distributed Algorithms (ADA).
1 code implementation • 2 Dec 2022 • Samuel Talkington, Daniel Turizo, Santiago Grijalva, Jorge Fernandez, Daniel K. Molzahn
Therefore, this paper addresses the conditions for estimating sensitivities of voltage magnitudes with respect to complex (active and reactive) electric power injections based on sensor measurements.
no code implementations • 17 Oct 2022 • Paprapee Buason, Sidhant Misra, Samuel Talkington, Daniel K. Molzahn
In this paper, we consider a sensor placement problem which seeks to identify locations for installing sensors that can capture all possible violations of voltage magnitude limits.
no code implementations • 9 Sep 2022 • Babak Taheri, Daniel K. Molzahn
Inspired by state estimation (SE) techniques, this paper proposes a new method for obtaining an AC power flow feasible point from the solution to a relaxed or approximated optimal power flow (OPF) problem.
no code implementations • 21 Apr 2022 • Babak Taheri, Daniel K. Molzahn, Santiago Grijalva
Using an extended version of the IEEE 123-bus test system, numerical simulations show that combining the ability to underground distribution lines with the deployment of mobile generators can significantly improve the resilience of the power supply to critical loads.
no code implementations • 13 Apr 2022 • Alyssa Kody, Amanda West, Daniel K. Molzahn
However, there may be many combinations of power lines whose de-energization will result in about the same reduction of system-wide wildfire risk, but the associated power outages affect different communities.
no code implementations • 18 Mar 2022 • Alyssa Kody, Ryan Piansky, Daniel K. Molzahn
To reduce wildfire ignition risks, power system operators preemptively de-energize high-risk power lines during extreme wildfire conditions as part of "Public Safety Power Shutoff" (PSPS) events.
no code implementations • 22 Oct 2021 • Sihan Zeng, Alyssa Kody, Youngdae Kim, Kibaek Kim, Daniel K. Molzahn
We train our RL policy using deep Q-learning, and show that this policy can result in significantly accelerated convergence (up to a 59% reduction in the number of iterations compared to existing, curvature-informed penalty parameter selection methods).
no code implementations • 31 May 2021 • Mohannad Alkhraijah, Carlos Menendez, Daniel K. Molzahn
Power system operators are increasingly looking toward distributed optimization to address various challenges facing electric power systems.
1 code implementation • 7 Aug 2019 • Sogol Babaeinejadsarookolaee, Adam Birchfield, Richard D. Christie, Carleton Coffrin, Christopher DeMarco, Ruisheng Diao, Michael Ferris, Stephane Fliscounakis, Scott Greene, Renke Huang, Cedric Josz, Roman Korab, Bernard Lesieutre, Jean Maeght, Daniel K. Molzahn, Thomas J. Overbye, Patrick Panciatici, Byungkwon Park, Jonathan Snodgrass, Ray Zimmerman
Consequently, benchmarking studies using the seminal AC Optimal Power Flow (AC-OPF) problem have emerged as the primary method for evaluating these emerging methods.
Optimization and Control