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1. Efficient Simulation of Cascading Outages Using an Energy Function-Embedded Quasi-Steady-State Model

   https://doi.org/10.1109/TPWRS.2025.3527811  

   Guo, Zhenping; Su, Xiaowen; Huang, Kaiyang; Sun, Kai; Simunovic, Srdjan; Chiang, Hsiao-Dong (January 2025, IEEE Transactions on Power Systems)

   This paper proposed an energy function-embedded quasi-steady-state model for efficient simulation of cascading outages on a power grid while addressing transient stability concerns. Compared to quasi-steady-state models, the proposed model incorporates short-term dynamic simulation and an energy function method to efficiently evaluate the transient stability of a power grid together with outage propagation without transient stability simulation. Cascading outage simulation using the proposed model conducts three steps for each disturbance such as a line outage. First, it performs time-domain simulation for a short term to obtain a post-disturbance trajectory. Second, along the trajectory, the system state with the local maximum potential energy is found and used as the initial point to search for a relevant unstable equilibrium by Newton's method. Third, the transient energy margin is estimated based on this unstable equilibrium to predict an out-of-step condition with generators. The proposed energy function-embedded quasi-steady-state model is tested in terms of its accuracy and time performance on an NPCC 140-bus power system and compared to a quasi-steady-state model embedding transient stability simulation. 

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2. Learning Cascading Failure Interactions by Deep Convolutional Generative Adversarial Network

   https://doi.org/10.1109/SmartGridComm52983.2022.9961045  

   Huang, Shuchen; Qi, Junjian (October 2022, 2022 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm))

   In this paper, a cascading failure interaction learning method is proposed for real utility outage data. For better revealing the structure, we reorganize the failure interaction matrix based on Louvain community detection. A deep convolutional generative adversarial network (DCGAN) based method is then proposed to learn the implicit features for failure propagation in the interaction matrix. A systematic method is further developed to evaluate the performance of the learning method on missing interaction recovery and new interaction discovery. The effectiveness of the proposed method is validated on the 14-year real utility outage data from Bonneville Power Administration. 

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3. Mitigation of Saturated Cut-sets During Multiple Outages to Enhance Power System Security

   https://doi.org/10.1109/TPWRS.2021.3076973  

   Sen Biswas, Reetam; Pal, Anamitra; Werho, Trevor; Vittal, Vijay (January 2021, IEEE Transactions on Power Systems)

   null (Ed.)

   Ensuring reliable operation of large power systems subjected to multiple outages is a challenging task because of the combinatorial nature of the problem. Traditional approaches for security assessment are often limited by their scope and/or speed, resulting in missing of critical contingencies that could lead to cascading failures. This paper proposes a two-component methodology to enhance power system security. The first component combines an efficient algorithm to detect cut-set saturation (called the feasibility test (FT) algorithm) with real-time contingency analysis (RTCA) to create an integrated corrective action (iCA), whose goal is to secure the system against cut-set saturation as well as critical branch overloads. The second component employs the only results of the FT to create a relaxed corrective action (rCA) to secure the system against post-contingency cut-set saturation. The first component is more comprehensive, but the latter is computationally more efficient. The effectiveness of the two components is evaluated based upon the number of cascade triggering contingencies alleviated, and the computation time. The results obtained by analyzing different case-studies on the IEEE 118-bus and 2000-bus synthetic Texas systems indicate that the proposed two-component methodology successfully enhances the scope and speed of power system security assessment during multiple outages. 

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4. Risk-based dynamic generation and transmission expansion planning with propagating effects of contingencies

   https://doi.org/10.1016/j.ijepes.2019.105762  

   Mehrtash, Mahdi; Kargarian, Amin (June 2020, International journal of electrical power energy systems)

   Transmission networks and generating units must be reinforced to satisfy the ever-increasing demand for electricity and to keep power system reliability within an acceptable level. According to the standards, the planned power system must be able to supply demand in the case of outage of a single element (N − 1 security criteria), and the possibility of cascading failures must be minimized. In this paper, we propose a risk-based dynamic generation and transmission expansion planning model with respect to the propagating effect of each contingency on the power system. Using the concept of risk, post-contingency load-shedding penalty costs are obtained and added in the objective function to penalize high-risk contingencies more dominantly. The McCormick relaxation is tailored to alter the objective function into a linear format. To keep the practicality of the proposed model, a second-order cone programming model is applied for power flow representation, and the problem is modeled in a dynamic time frame. The proposed model is formulated as a mixed-integer second-order cone programming problem. The numerical studies on the RTS 24-bus test system illustrate the efficacy of the proposed model. 

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5. Hybrid Physics and Data-driven Contingency Filtering for Security Operation of Micro Energy-water Nexus

   https://doi.org/10.17775/CSEEJPES.2022.07240  

   Goodarzi, Mostafa; Li, Qifeng (January 2023, CSEE Journal of Power and Energy Systems)

   This paper investigates a novel engineering problem, i.e., security-constrained multi-period operation of micro energy-water nexuses. This problem is computationally challenging because of its high nonlinearity, nonconvexity, and large dimension. We propose a two-stage iterative algorithm employing a hybrid physics and data-driven contingency filtering (CF) method and convexification to solve it. The convexified master problem is solved in the first stage by considering the base case operation and binding contingencies set (BCS). The second stage updates BCS using physics-based data-driven methods, which include dynamic and filtered data sets. This method is faster than existing CF methods because it relies on offline optimization problems and contains a limited number of online optimization problems. We validate effectiveness of the proposed method using two different case studies: the IEEE 13-bus power system with the EPANET 8-node water system and the IEEE 33-bus power system with the Otsfeld 13-node water system. 

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