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ORCIDDaniel K. Molzahn
Person information
- affiliation: Georgia Institute of Technology, GA, USA
- affiliation: Argonne National Laboratory
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2020 – today
- 2024
[j14]Gustav Nilsson
, Alejandro D. Owen Aquino, Samuel Coogan, Daniel K. Molzahn:
GreenEVT: Greensboro Electric Vehicle Testbed. IEEE Syst. J. 18(1): 600-611 (2024)- [c28]Rachel Harris, Daniel K. Molzahn:
Detecting and Mitigating Data Integrity Attacks on Distributed Algorithms for Optimal Power Flow using Machine Learning. HICSS 2024: 3170-3181 - [i39]Rahul K. Gupta, Paprapee Buason, Daniel K. Molzahn:
Fairness-aware Photovoltaic Generation Limits for Voltage Regulation in Power Distribution Networks using Conservative Linear Approximations. CoRR abs/2401.07404 (2024) - [i38]Mohannad Alkhraijah, Daniel K. Molzahn:
A Fault-Tolerant Distributed Termination Method for Distributed Optimization Algorithms. CoRR abs/2401.16595 (2024) - [i37]Rahul K. Gupta, Daniel K. Molzahn:
Improving Fairness in Photovoltaic Curtailments via Daily Topology Reconfiguration for Voltage Control in Power Distribution Networks. CoRR abs/2403.07853 (2024) - [i36]Rahul K. Gupta, Daniel K. Molzahn:
Analysis of Fairness-promoting Optimization Schemes of Photovoltaic Curtailments for Voltage Regulation in Power Distribution Networks. CoRR abs/2404.00394 (2024) - [i35]Paprapee Buason, Sidhant Misra, Jean-Paul Watson, Daniel K. Molzahn:
Adaptive Power Flow Approximations with Second-Order Sensitivity Insights. CoRR abs/2404.04391 (2024) - [i34]Babak Taheri, Rahul K. Gupta, Daniel K. Molzahn:
Optimized LinDistFlow for High-Fidelity Power Flow Modeling of Distribution Networks. CoRR abs/2404.05125 (2024) - [i33]Paprapee Buason, Sidhant Misra, Daniel K. Molzahn:
Sample-Based Conservative Bias Linear Power Flow Approximations. CoRR abs/2404.09876 (2024) - [i32]Madeleine Pollack, Ryan Piansky, Swati Gupta, Alyssa Kody, Daniel K. Molzahn:
Equitably allocating wildfire resilience investments for power grids: The curse of aggregation and vulnerability indices. CoRR abs/2404.11520 (2024) - [i31]Ryan Piansky, Georgia Stinchfield, Alyssa Kody, Daniel K. Molzahn, Jean-Paul Watson:
Long Duration Battery Sizing, Siting, and Operation Under Wildfire Risk Using Progressive Hedging. CoRR abs/2404.12296 (2024) - [i30]Daniel Turizo, Diego Cifuentes, Anton Leykin, Daniel K. Molzahn:
Discrete Shortest Paths in Optimal Power Flow Feasible Regions. CoRR abs/2408.02172 (2024) - [i29]Rosemarie Santa González, Ryan Piansky, Sue M. Bae, Justin B. Biddle, Daniel K. Molzahn:
Beyond Algorithmic Fairness: A Guide to Develop and Deploy Ethical AI-Enabled Decision-Support Tools. CoRR abs/2409.11489 (2024) - [i28]Ryan Piansky, Sofia Taylor, Noah Rhodes, Daniel K. Molzahn, Line A. Roald, Jean-Paul Watson:
Quantifying Metrics for Wildfire Ignition Risk from Geographic Data in Power Shutoff Decision-Making. CoRR abs/2409.20511 (2024) - [i27]Babak Taheri, Daniel K. Molzahn:
Improving the Accuracy of DC Optimal Power Flow Formulations via Parameter Optimization. CoRR abs/2410.11725 (2024) - [i26]Samuel Talkington, Rahul K. Gupta, Richard Asiamah, Paprapee Buason, Daniel K. Molzahn:
Strategic Electric Distribution Network Sensing via Spectral Bandits. CoRR abs/2410.21270 (2024) - 2023
- [j13]Ignacio Aravena, Daniel K. Molzahn, Shixuan Zhang, Cosmin G. Petra, Frank E. Curtis, Shenyinying Tu, Andreas Wächter, Ermin Wei, Elizabeth Wong, Amin Gholami, Kaizhao Sun, Xu Andy Sun, Stephen T. Elbert, Jesse Holzer, Arun Veeramany:
Recent Developments in Security-Constrained AC Optimal Power Flow: Overview of Challenge 1 in the ARPA-E Grid Optimization Competition. Oper. Res. 71(6): 1997-2014 (2023) - [j12]Frank E. Curtis, Daniel K. Molzahn, Shenyinying Tu, Andreas Wächter, Ermin Wei, Elizabeth Wong:
A Decomposition Algorithm with Fast Identification of Critical Contingencies for Large-Scale Security-Constrained AC-OPF. Oper. Res. 71(6): 2031-2044 (2023) - [j11]Olga Kuryatnikova, Bissan Ghaddar, Daniel K. Molzahn:
Two-Stage Robust Quadratic Optimization with Equalities and Its Application to Optimal Power Flow. SIAM J. Optim. 33(4): 2830-2857 (2023) - [c27]Babak Taheri, Daniel K. Molzahn:
Restoring AC Power Flow Feasibility from Relaxed and Approximated Optimal Power Flow Models. ACC 2023: 4463-4470 - [c26]Alejandro Owen Aquino, Rachel Harris, Alyssa Kody, Daniel K. Molzahn:
Comparing Machine Learning and Optimization Approaches for the N - k Interdiction Problem Considering Load Variability. HICSS 2023: 2766-2775 - [i25]Mohannad Alkhraijah, Rachel Harris, Carleton Coffrin, Daniel K. Molzahn:
PowerModelsADA: A Framework for Solving Optimal Power Flow using Distributed Algorithms. CoRR abs/2304.00639 (2023) - [i24]Babak Taheri, Daniel K. Molzahn:
AC Power Flow Feasibility Restoration via a State Estimation-Based Post-Processing Algorithm. CoRR abs/2304.11418 (2023) - [i23]Gustav Nilsson, Alejandro D. Owen Aquino, Samuel Coogan, Daniel K. Molzahn:
GreenEVT: Greensboro Electric Vehicle Testbed. CoRR abs/2305.12722 (2023) - [i22]Ali Jalilian, Babak Taheri, Daniel K. Molzahn:
Co-Optimization of Damage Assessment and Restoration: A Resilience-Driven Dynamic Crew Allocation for Power Distribution Systems. CoRR abs/2309.08704 (2023) - [i21]Babak Taheri, Daniel K. Molzahn:
Optimizing Parameters of the DC Power Flow. CoRR abs/2310.00447 (2023) - [i20]Mohannad Alkhraijah, Rachel Harris, Samuel Litchfield, David Huggins, Daniel K. Molzahn:
Detecting Shared Data Manipulation in Distributed Optimization Algorithms. CoRR abs/2310.13252 (2023) - [i19]Rachel Harris, Mohannad Alkhraijah, Daniel K. Molzahn:
Optimally Managing the Impacts of Convergence Tolerance for Distributed Optimal Power Flow. CoRR abs/2311.08305 (2023) - [i18]Babak Taheri, Daniel K. Molzahn:
AC Power Flow Informed Parameter Learning for DC Power Flow Network Equivalents. CoRR abs/2311.13104 (2023) - [i17]Alejandro D. Owen Aquino, Samuel Talkington, Daniel K. Molzahn:
Managing Vehicle Charging During Emergencies via Conservative Distribution System Modeling. CoRR abs/2311.16975 (2023) - 2022
- [j10]Jianzhe Liu, Bai Cui, Daniel K. Molzahn, Chen Chen, Xiaonan Lu, Feng Qiu:
Optimal Power Flow in DC Networks With Robust Feasibility and Stability Guarantees. IEEE Trans. Control. Netw. Syst. 9(2): 904-916 (2022) - [c25]Alyssa Kody, Amanda West, Daniel K. Molzahn:
Sharing the Load: Considering Fairness in De-energization Scheduling to Mitigate Wildfire Ignition Risk using Rolling Optimization. CDC 2022: 5705-5712 - [i16]Alyssa Kody, Ryan Piansky, Daniel K. Molzahn:
Optimizing Transmission Infrastructure Investments to Support Line De-energization for Mitigating Wildfire Ignition Risk. CoRR abs/2203.10176 (2022) - [i15]Alyssa Kody, Amanda West, Daniel K. Molzahn:
Sharing the Load: Considering Fairness in De-energization Scheduling to Mitigate Wildfire Ignition Risk using Rolling Optimization. CoRR abs/2204.06543 (2022) - [i14]Babak Taheri, Daniel K. Molzahn, Santiago Grijalva:
Improving Distribution System Resilience by Undergrounding Lines and Deploying Mobile Generators. CoRR abs/2204.10407 (2022) - [i13]Babak Taheri, Daniel K. Molzahn:
Restoring AC Power Flow Feasibility from Relaxed and Approximated Optimal Power Flow Models. CoRR abs/2209.04399 (2022) - [i12]Paprapee Buason, Sidhant Misra, Daniel K. Molzahn:
A Data-Driven Method for Locating Sensors and Selecting Alarm Thresholds to Identify Violations of Voltage Limits in Distribution Systems. CoRR abs/2210.09414 (2022) - [i11]Samuel Talkington, Daniel Turizo, Santiago Grijalva, Jorge Fernandez, Daniel K. Molzahn:
Conditions for Estimation of Sensitivities of Voltage Magnitudes to Complex Power Injections. CoRR abs/2212.01471 (2022) - 2021
- [c24]Sikun Xu, Ruoyi Ma, Daniel K. Molzahn, Hassan L. Hijazi, Cédric Josz:
Verifying Global Optimality of Candidate Solutions to Polynomial Optimization Problems using a Determinant Relaxation Hierarchy. CDC 2021: 3143-3148 - [i10]Cyrus Hettle, Swati Gupta, Daniel K. Molzahn:
Fair and Reliable Reconnections for Temporary Disruptions in Electric Distribution Networks using Submodularity. CoRR abs/2104.07631 (2021) - [i9]Mohannad Alkhraijah, Carlos Menendez, Daniel K. Molzahn:
Evaluating the Performance of Distributed Optimal Power Flow Algorithms with Nonideal Communication. CoRR abs/2106.00135 (2021) - [i8]Alyssa Kody, Samuel Chevalier, Spyros Chatzivasileiadis, Daniel K. Molzahn:
Modeling the AC Power Flow Equations with Optimally Compact Neural Networks: Application to Unit Commitment. CoRR abs/2110.11269 (2021) - [i7]Sihan Zeng, Alyssa Kody, Youngdae Kim, Kibaek Kim, Daniel K. Molzahn:
A Reinforcement Learning Approach to Parameter Selection for Distributed Optimization in Power Systems. CoRR abs/2110.11991 (2021) - 2020
- [i6]Daniel Turizo, Daniel K. Molzahn:
Invertibility Conditions for the Admittance Matrices of Balanced Power Systems. CoRR abs/2012.04087 (2020)
2010 – 2019
- 2019
- [j9]Mengqi Yao, Daniel K. Molzahn, Johanna L. Mathieu:
An Optimal Power-Flow Approach to Improve Power System Voltage Stability Using Demand Response. IEEE Trans. Control. Netw. Syst. 6(3): 1015-1025 (2019) - [j8]Daniel K. Molzahn, Jianhui Wang:
Detection and Characterization of Intrusions to Network Parameter Data in Electric Power Systems. IEEE Trans. Smart Grid 10(4): 3919-3928 (2019) - [c23]Line A. Roald, Daniel K. Molzahn:
Implied Constraint Satisfaction in Power System optimization: The Impacts of Load Variations. Allerton 2019: 308-315 - [c22]Daniel K. Molzahn, Line A. Roald:
Grid-Aware versus Grid-Agnostic Distribution System Control: A Method for Certifying Engineering Constraint Satisfaction. HICSS 2019: 1-10 - [c21]Cédric Josz, Daniel K. Molzahn, Matteo Tacchi, Somayeh Sojoudi:
Transient Stability Analysis of Power Systems via Occupation Measures. ISGT 2019: 1-5 - [i5]Sogol Babaeinejadsarookolaee, Adam B. Birchfield, Richard D. Christie, Carleton Coffrin, Christopher L. DeMarco, Ruisheng Diao, Michael Ferris, Stephane Fliscounakis, Scott Greene, Renke Huang, Cédric Josz, Roman Korab, Bernard C. Lesieutre, Jean Maeght, Daniel K. Molzahn, Thomas J. Overbye, Patrick Panciatici, Byungkwon Park, Jonathan Snodgrass, Ray Zimmerman:
The Power Grid Library for Benchmarking AC Optimal Power Flow Algorithms. CoRR abs/1908.02788 (2019) - [i4]Andreas Venzke, Daniel K. Molzahn, Spyros Chatzivasileiadis:
Efficient Creation of Datasets for Data-Driven Power System Applications. CoRR abs/1910.01794 (2019) - 2018
- [j7]Owen Coss, Jonathan D. Hauenstein, Hoon Hong, Daniel K. Molzahn:
Locating and Counting Equilibria of the Kuramoto Model with Rank-One Coupling. SIAM J. Appl. Algebra Geom. 2(1): 45-71 (2018) - [j6]Cédric Josz, Daniel K. Molzahn:
Lasserre Hierarchy for Large Scale Polynomial Optimization in Real and Complex Variables. SIAM J. Optim. 28(2): 1017-1048 (2018) - [j5]Daniel K. Molzahn:
Identifying and Characterizing Non-Convexities in Feasible Spaces of Optimal Power Flow Problems. IEEE Trans. Circuits Syst. II Express Briefs 65-II(5): 672-676 (2018) - [c20]Mohammad Rasoul Narimani, Daniel K. Molzahn, Dan Wu, Mariesa L. Crow:
Empirical Investigation of Non-Convexities in Optimal Power Flow Problems. ACC 2018: 3847-3854 - [c19]Mohammad Rasoul Narimani, Daniel K. Molzahn, Harsha Nagarajan, Mariesa L. Crow:
Comparison of Various Trilinear Monomial Envelopes for Convex Relaxations of Optimal Power Flow Problems. GlobalSIP 2018: 865-869 - 2017
- [j4]Javad Lavaei, Steven H. Low, Ross Baldick, Baosen Zhang, Daniel K. Molzahn, Florian Dörfler, Henrik Sandberg:
Guest Editorial Distributed Control and Efficient Optimization Methods for Smart Grid. IEEE Trans. Smart Grid 8(6): 2939-2940 (2017) - [j3]Daniel K. Molzahn, Florian Dörfler, Henrik Sandberg, Steven H. Low, Sambuddha Chakrabarti, Ross Baldick, Javad Lavaei:
A Survey of Distributed Optimization and Control Algorithms for Electric Power Systems. IEEE Trans. Smart Grid 8(6): 2941-2962 (2017) - [c18]Mengqi Yao, Daniel K. Molzahn, Johanna L. Mathieu:
The impact of load models in an algorithm for improving voltage stability via demand response. Allerton 2017: 149-156 - 2016
- [j2]Daniel K. Molzahn, Ian A. Hiskens:
Convex Relaxations of Optimal Power Flow Problems: An Illustrative Example. IEEE Trans. Circuits Syst. I Regul. Pap. 63-I(5): 650-660 (2016) - [c17]Dhagash Mehta, Daniel K. Molzahn, Konstantin S. Turitsyn:
Recent advances in computational methods for the power flow equations. ACC 2016: 1753-1765 - [c16]Daniel K. Molzahn:
Introduction to the power flow equations and moment/sum-of-squares relaxations of optimal power flow problems. ACC 2016: 1766 - [c15]Daniel K. Molzahn, Dhagash Mehta, Matthew E. Niemerg:
Toward topologically based upper bounds on the number of power flow solutions. ACC 2016: 5927-5932 - [c14]Krishnamurthy Dvijotham, Daniel K. Molzahn:
Error bounds on the DC power flow approximation: A convex relaxation approach. CDC 2016: 2411-2418 - [c13]Daniel K. Molzahn, Cédric Josz, Ian A. Hiskens:
Moment relaxations of optimal power flow problems: Beyond the convex hull. GlobalSIP 2016: 856-860 - [c12]Daniel K. Molzahn, Ian A. Hiskens, Bernard C. Lesieutre:
Calculation of Voltage Stability Margins and Certification of Power Flow Insolvability Using Second-Order Cone Programming. HICSS 2016: 2307-2316 - [c11]Daniel K. Molzahn, Ian A. Hiskens, Cédric Josz, Patrick Panciatici:
Computational analysis of sparsity-exploiting moment relaxations of the OPF problem. PSCC 2016: 1-7 - [i3]Daniel K. Molzahn, Matthew E. Niemerg, Dhagash Mehta, Jonathan D. Hauenstein:
Investigating the Maximum Number of Real Solutions to the Power Flow Equations: Analysis of Lossless Four-Bus Systems. CoRR abs/1603.05908 (2016) - 2015
- [c10]Daniel K. Molzahn, Cédric Josz, Ian A. Hiskens, Patrick Panciatici:
Solution of optimal power flow problems using moment relaxations augmented with objective function penalization. CDC 2015: 31-38 - [c9]Daniel K. Molzahn, Sina S. Baghsorkhi, Ian A. Hiskens:
Semidefinite relaxations of equivalent optimal power flow problems: An illustrative example. ISCAS 2015: 1887-1890 - [i2]Daniel K. Molzahn, Dhagash Mehta, Matthew E. Niemerg:
Toward Topologically Based Upper Bounds on the Number of Power Flow Solutions. CoRR abs/1509.09227 (2015) - [i1]Dhagash Mehta, Daniel K. Molzahn, Konstantin S. Turitsyn:
Recent Advances in Computational Methods for the Power Flow Equations. CoRR abs/1510.00073 (2015) - 2014
- [c8]Daniel K. Molzahn, Bernard C. Lesieutre, Christopher L. DeMarco:
Investigation of Non-zero Duality Gap Solutions to a Semidefinite Relaxation of the Optimal Power Flow Problem. HICSS 2014: 2325-2334 - [c7]Daniel K. Molzahn, Ian A. Hiskens:
Moment-based relaxation of the optimal power flow problem. PSCC 2014: 1-7 - [c6]Patrick Panciatici, Marco C. Campi, Simone Garatti, Steven H. Low, Daniel K. Molzahn, Andy X. Sun, Louis Wehenkel:
Advanced optimization methods for power systems. PSCC 2014: 1-18 - 2013
- [j1]Daniel K. Molzahn, Bernard C. Lesieutre:
Initializing Dynamic Power System Simulations Using Eigenvalue Formulations of the Induction Machine and Power Flow Models. IEEE Trans. Circuits Syst. I Regul. Pap. 60-I(3): 690-702 (2013) - [c5]Alexander R. Borden, Daniel K. Molzahn, Bernard C. Lesieutre, Parmeswaran Ramanathan:
Power system structure and confidentiality preserving transformation of Optimal Power Flow problem. Allerton 2013: 1021-1028 - 2012
- [c4]Alexander R. Borden, Daniel K. Molzahn, Parmeswaran Ramanathan, Bernard C. Lesieutre:
Confidentiality-preserving optimal power flow for cloud computing. Allerton Conference 2012: 1300-1307 - 2011
- [c3]Bernard C. Lesieutre, Daniel K. Molzahn, Alexander R. Borden, Christopher L. DeMarco:
Examining the limits of the application of semidefinite programming to power flow problems. Allerton 2011: 1492-1499 - [c2]Daniel R. Schwarting, Daniel K. Molzahn, Christopher L. DeMarco, Bernard C. Lesieutre:
Topological and Impedance Element Ranking (TIER) of the Bulk-Power System. HICSS 2011: 1-10 - 2010
- [c1]Daniel K. Molzahn, Bernard C. Lesieutre:
An eigenvalue formulation for determining initial conditions of induction machines in dynamic power system simulations. ISCAS 2010: 2311-2313
Coauthor Index
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