Mostafa Sahraei-Ardakani

Xingpeng Li, Pranavamoorthy Balasubramanian, Mostafa Sahraei-Ardakani et al.

Transmission switching (TS) has gained significant attention recently. However, barriers still remain and must be overcome before the technology can be adopted by the industry. The state of the art challenges include AC feasibility and performance, computational complexity, the ability to handle large-scale real power systems, and dynamic stability. This two-part paper investigates these challenges by developing an AC TS-based real-time contingency analysis (RTCA) tool that can handle large-scale systems within a reasonable time. The tool proposes multiple corrective switching actions, after detection of a contingency with potential violations. To reduce the computational complexity, three heuristic algorithms are proposed to generate a small set of candidates for switching. Parallel computing is implemented to further speed up the solution time. Furthermore, stability analysis is performed to check for dynamic stability of proposed TS solutions. Part I of the paper presents a comprehensive literature review and the methodology. The promising results, tested on the Tennessee Valley Authority (TVA) system and actual energy management system (EMS) snapshots from Pennsylvania New Jersey Maryland (PJM) and the Electric Reliability Council of Texas (ERCOT), are presented in Part II. It is concluded that RTCA with corrective TS significantly reduces potential post-contingency violations and is ripe for industry adoption.

Sayed Abdullah Sadat, Mostafa Sahraei-Ardakani

After decades of research, cascading blackouts remain one of the unresolved challenges in the bulk power system operations. A new perspective for measuring the susceptibility of the system to cascading failures is clearly needed. The newly developed concept of system stress metrics may be able to provide new insights into this problem. The method measures stress as susceptibility to cascading failures by analyzing the network structure and electrical properties. This paper investigates the effectiveness of transmission switching in reducing the risk of cascading failures, measured in system stress metrics. Based on line-outage distribution factors, an algorithm is developed to identify and test the switching candidates quickly. A case study analyzing different stress metrics on the IEEE 118-bus test system is presented. The results show that transmission switching identified by our proposed algorithm could be used in preventive as well as corrective mechanisms to reduce the system's susceptibility to cascading failures. Contrary to the conventional operation wisdom that switching lines out of service jeopardizes reliability, our results suggest the opposite; system operators can often use transmission switching, when the system is under stress, as a tool to reduce the risk of cascading failures.

Sayed Abdullah Sadat, Mostafa Sahraei-Ardakani

A Successive linear programming (SLP) approach is one of the favorable approaches for solving large scale nonlinear optimization problems. Solving an alternating current optimal power flow (ACOPF) problem is no exception, particularly considering the large real-world transmission networks across the country. It is, however, essential to improve the computational performance of the SLP algorithm. One way to achieve this goal is through the efficient initialization of the algorithm with a near-optimal solution. This paper examines various machine learning (ML) algorithms available in the Scikit-Learn library to initialize an SLP-ACOPF solver, including examining linear and nonlinear ML algorithms. We evaluate the quality of each of these machine learning algorithms for predicting variables needed for a power flow solution. The solution is then used as an initialization for an SLP-ACOPF algorithm. The approach is tested on a congested and non-congested 3 bus systems. The results obtained from the best-performed ML algorithm in this work are compared with the results of a DCOPF solution for the initialization of an SLP-ACOPF solver.