The frequency of extreme events (e.g., hurricanes, earthquakes, and floods) and man-made attacks (cyber and physical attacks) has increased dramatically in recent years. These events have severely impacted power systems ranging from long outage times to major equipment (e.g., substations, transmission lines, and power plants) destructions. This calls for developing control and operation methods and planning strategies to improve grid resilience against such events. The first step toward this goal is to develop resilience metrics and evaluation methods to compare planning and operation alternatives and to provide techno-economic justifications for resilience enhancement. Although several power system resilience definitions, metrics, and evaluation methods have been proposed in the literature, they have not been universally accepted or standardized. This paper provides a comprehensive and critical review of current practices of power system resilience metrics and evaluation methods and discusses future directions and recommendations to contribute to the development of universally accepted and standardized definitions, metrics, evaluation methods, and enhancement strategies. This paper thoroughly examines the consensus on the power system resilience concept provided by different organizations and scholars and existing and currently practiced resilience enhancement methods. Research gaps, associated challenges, and potential solutions to existing limitations are also provided.

An analytical method for placement and sizing of distributed generation on power distribution systems for loss reduction is introduced. The proposed analytical method is developed based on a new formulation for the power flow problem, which is non‐iterative, direct, and involves no convergence issues even for systems with high R / X branch ratios. Further, this power flow solution is extremely useful whenever fast and repetitive power flow estimations are required. A priority list based on loss sensitivity factors is developed to determine the optimal locations of the candidate distributed generation units. Sensitivity analysis is performed to estimate the optimal size and power factor of the candidate distributed generation units. Various types of distributed generators (DGs) have been dealt with and viable solutions are proposed to reduce total system loss. The proposed method has been tested on 33‐bus and 69‐bus distribution systems, which are extensively used as examples in solving the placement and sizing problem of DGs. Exhaustive power flow routines are also performed to verify the sizes obtained by the analytical method. The test results show that the proposed analytical method could lead to optimal or near‐optimal solution, while requiring lower computational effort.

Existing power system resilience enhancement methods, such as proactive generation rescheduling, movable sources dispatch, and network topology reconfiguration, do not explore the capability and flexibility of shunts to maintain voltage stability during and after disrupting events. Besides, existing methods rely on accurate system models that are not easily scalable for large integrated power grids. In this paper, a data-driven multi-agent framework based on a deep-reinforcement-learning algorithm is proposed to overcome the computation and scalability concerns related to precise system models and to plan for the deployment of shunts for power system resilience enhancement. Specifically, voltage violations due to outages of multiple lines during windstorms are taken as an example of a power system resilience improvement problem. Then, a multi-agent based hybrid soft actor critic (HSAC) algorithm is developed for offline siting and sizing as well as online controlling of shunt reactive power compensators to enhance voltage resilience. The HSAC algorithm is derived from the fundamental SAC algorithms that contain both continuous and discrete action spaces. The proposed multi-agent framework learns from previous experiences and eventually gets trained to determine proper locations and sizes for shunts to avoid voltage violations during multiple line failures. The proposed approach is demonstrated on the IEEE 57-bus and IEEE 300-bus systems. The results show that the proposed multi-agent framework is effective for installation planning and controlling of shunts to enhance power system resilience.

This paper presents an analytical method to model the output of large wind farms for power system reliability assessment. The model considers the variability of wind, random failures (forced outages) of turbines, as well as the correlation between turbine outputs that results from the turbines on a farm being subjected to similar incident wind speeds. Due to this correlation, the outputs of individual turbines cannot be modeled as independent random variables. The problem is addressed here by separately modeling the independent outages of wind turbines and the dependency on wind speed, and then convolving the two distributions. The resulting model includes both probability and frequency distributions of the power output of the wind farm. The proposed method is demonstrated on the IEEE Reliability Test System (IEEE RTS). Monte Carlo simulation is used to validate the results.

This paper proposes a comprehensive evaluation of stacked revenue generated from grid-connected energy storage systems (ESSs). The stacked revenue from an ESS cannot be calculated by merely aggregating the benefits from various applications (e.g., energy arbitrage, frequency regulation, and outage mitigation) as the ESS may not be available for all types of applications during the same time interval---because a quantity committed to one market may not be committed to another. In this paper, different types of applications for grid-connected ESSs are identified, and a model incorporating component reliability, power system operation constraints, and storage system operation constraints is developed to evaluate the composite revenue generated from these applications. In this model, the types of applications of ESSs are prioritized according to their intended contributions and system operating conditions. Sequential Monte Carlo simulation is used for evaluating value from the reliability improvement and a quadratically constrained linear programming model is built for estimating the maximum revenue from arbitrage and regulation markets. The proposed method is demonstrated on the IEEE reliability test system (IEEE-RTS) with the historical PJM data.

Catastrophic impacts of wildfires on the performance of power grids have increased in the recent years. Though various methods have been applied to enhance power grid resilience against severe weather events, only a few have focused on wildfires. Most previous operational-based resilience enhancement methods have focused on corrective or restorative strategies during and after extreme events without proactively preparing the system for forecasted potential failures. Also, the propagation behavior of wildfires among system components induces further complexities resulting in a mathematically involved problem accompanied with many modeling challenges. During sequential failures, operators need to make decisions in a fast-paced manner to maintain reliable operation and avoid cascading failures and blackouts. Thereby, the complexity of decision processes increases dramatically during extreme weather events. This article proposes a probabilistic proactive generation redispatch strategy to enhance the operational resilience of power grids during wildfires. A Markov decision process is used to model system state transitions and to provide generation redispatch strategies for each possible system state given component failure probabilities, wildfire spatiotemporal properties, and load variation. For a realistic system representation, dynamic system constraints are considered including ramping rates and minimum up/down times of generating units, load demand profile, and transmission constraints. The IBM ILOG CPLEX optimization studio is utilized to solve the optimization problem. The IEEE 30-bus system is used to validate the proposed strategy under various impact scenarios. The results demonstrate the effectiveness of the proposed method in enhancing the resilience level of power grids during wildfires.