Resilience Analysis and Cascading Failure Modeling of Power Systems Under Extreme Temperatures

Abstract

In this paper, we propose an AC power flow based cascading failure model that explicitly considers external weather conditions, extreme temperatures in particular, and evaluates the impact of extreme temperature on the initiation and propagation of cascading blackouts. Based on this model, resilience analysis of the power system is performed with extreme temperatures. Specifically, the changes of load and dynamic line rating are modeled due to temperature disturbance. The probabilities for transmission line and generator outages are evaluated, and the timing for each type of events is calculated to decide the actual event sequence. It should be emphasized that the correlated events, in the advent of external temperature changes, could contribute to voltage instability. Besides, we model undervoltage load shedding and operator re-dispatch as control strategies for preventing the propagation of cascading failures. The effectiveness of the proposed model is verified by simulation results on the RTS-96 3-area system. It is found that temperature disturbances can lead to correlated load change and line/generator tripping, which will greatly increase the risk of cascading and voltage instability. Critical temperature change, critical area with temperature disturbance, the identification of most vulnerable buses, and the comparison of different control strategies are also investigated.

Generator Parameter Estimation by Q-Learning Based on PMU Measurements

“…The values of Q are initialized to be zero and are updated repeatedly by (5) based on the action reward in the current state and the maximum reward in the next state. The algorithm will converge to the optimal policy, Q * , after N episodes.…”

“…In power systems, monitoring, protection, and control are usually model-based, an accurate dynamic model for either synchronous generators [1]- [3] or inverters [4] is thus essential. The inaccuracy of the power system model has been witnessed in the blackout occurred in Western U.S. in 1996 [1], in which model simulations showed a stable response while the system became unstable [5], [6]. The synchronous generator is one of the most critical components in power systems and its accurate modeling is important for studying the dynamics of the system.…”

Proactive Scheduling of Hydrogen Systems for Resilience Enhancement of Distribution Networks

“…Recently, due to the large deployment of distributed energy resources (DERs) and other smart grid technologies, power systems are becoming more complex and vulnerable to extreme events. These extreme events can significantly affect the operation of power systems and results in outages and even cascading failures [1]. For instance, Hurricane Sandy (October 22-November 2, 2012) affected 7.5 million electricity consumers (mainly on the distribution side) with $65 billion in damage [2].…”

Impacts of Privately Owned Electric Vehicles on Distribution System Resilience: A Multi-agent Optimization Approach

“…Power system resilience reflects the versatility of a system to withstand and recover rapidly from unexpected disruptions [1,2]. An increasing market penetration of private electric vehicles (EVs) provides new opportunities for enhancing power system resilience due to their mobility and fast regulating characteristics [3].…”

“…We address the aforementioned gaps by proposing a a framework of network-based multi-agent optimization problems with equilibrium constraints (N-MOPEC) in a coupled distribution and transportation system. The main contribution is two-fold: (1) The modeling framework captures the decentralized behavior of stakeholders during distribution system restoration process and the spatial and temporal interdependence between transportation and power systems, which allows for rigorous system analyses and optimal V2G incentives design. (2) To facilitate large-scale computation, we reformulate the multi-agent optimization problems as an exact convex optimization problem, which can be efficiently solved by commercial nonlinear solvers.…”