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Although electricity transmission systems are typically very robust, the impacts that arise when they are disrupted motivate methods for analyzing outage risk. For example, N-k interdiction models were developed to characterize disruptions by identifying the sets of k power system components whose failure results in “worst case” outages. While such models have advanced considerably, they generally neglect how failures outside the power system can cause large-scale outages. Specifically, failures in natural gas pipeline networks that provide fuel for gas-fired generators can affect the function of the power grid. In this study, we extend N-k interdiction modeling to gas pipeline networks. We use recently developed convex relaxations for natural gas flow equations to yield tractable formulations for identifying sets of k components whose failure can cause curtailment of natural gas delivery. We then present a novel cutting-plane algorithm to solve these problems. Finally, we use test instances to analyze the performance of the approach in conjunction with simulations of outage effects on electrical power grids.
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Enhancing ACPF Analysis: Integrating Newton-Raphson Method with Gradient Descent and Computational Graphs
This manuscript presents a novel approach utilizing computational graph strategies for solving the power flow equations through the synergistic use of Newton-Raphson (NR) and Gradient Descent (GD). As a foundational element for operational and strategic decision-making in electrical networks, the power flow analysis has been rigorously examined for decades. Conventional solution techniques typically depend on second-order processes, which may falter, especially when faced with subpar starting values or during heightened system demands. These issues are becoming more acute with the dynamic shifts in generation and consumption patterns within modern electrical systems. Our research introduces a dual-mode algorithm that amalgamates the principles of first-order operation. This inventive method is adept at circumventing potential local minima traps that hinder current methodologies, thereby reinforcing the dependability of power flow solutions. We substantiate the effectiveness of our advanced algorithm with comprehensive testing on established IEEE benchmark systems. Our findings reveal that our approach not only expedites the convergence process but also ensures consistent performance across diverse system states, signifying a meaningful progression in the realm of power flow computation.
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- Award ID(s):
- 1851602
- PAR ID:
- 10515294
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3503-3120-2
- Page Range / eLocation ID:
- 01 to 05
- Format(s):
- Medium: X
- Location:
- College Station, TX, USA
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/TPEC60005.2024.10472209
