Risk Assessment and Mitigation of Cascading Failures Using Critical Line Sensitivities

Abstract

Security concerns have been raised about cascading failure risks in evolving power grids. This paper reveals, for the first time, that the risk of cascading failures can be increased at low network demand levels when considering security-constrained generation dispatch. This occurs because critical transmission corridors become very highly loaded due to the presence of centralized generation dispatch, e.g., large thermal plants far from demand centers. This increased cascading risk is revealed in this work by incorporating security-constrained generation dispatch into the risk assessment and mitigation of cascading failures. A security-constrained AC optimal power flow, which considers economic functions and security constraints (e.g., network constraints, 𝑵 − 𝟏 security, and generation margin), is used to provide a representative day-ahead operational plan. Cascading failures are simulated using two simulators, a quasi-steady state DC power flow model, and a dynamic model incorporating all frequency-related dynamics, to allow for result comparison and verification. The risk assessment procedure is illustrated using synthetic networks of 200 and 2,000 buses. Further, a novel preventive mitigation measure is proposed to first identify critical lines, whose failures are likely to trigger cascading failures, and then to limit power flow through these critical lines during dispatch. Results show that shifting power equivalent to 1% of total demand from critical lines to other lines can reduce cascading risk by up to 80%.

A Fast Reliability Evaluation Strategy for Power Systems under High Proportional Renewable Energy—A Hybrid Data-Driven Method

“…At present, power system reliability evaluation algorithms to be applied in engineering can be divided into two categories: a state analysis method and Monte Carlo simulation (MCS) method [5]. The evaluation process of these two methods is basically the same, usually consisting of three parts: system state selection, system state evaluation, and indicator statistics [6,7]. Among them, system state evaluation usually requires performing optimal power Fflow (OPF) on system state samples to determine the minimum load shedding of the system.…”