Energy Infrastructures are perceived continuously vulnerable to a range of high-impact low-probability (HILP) incidents-e.g., earthquakes, tsunamis, floods, windstorms, etc.-the resilience to which is high on demand. Specifically suited to battery energy storage system (BESS) solutions, this paper presents a new resilience-driven algorithm for hardening power distribution systems against earthquakes. The concept of fragility curve is applied to characterize an earthquake hazard, assess its impact on power distribution systems, and estimate the unavailability of the network elements when exposed to extreme earthquakes. A new metric is defined to quantify the network resilience taking into account the uncertain nature of such HILP events. A linear programming (LP) optimization problem is formulated to determine the capacity and location of the BESSs for enhanced resilience against earthquakes. Efficacy of the proposed framework is numerically analyzed and verified through application to a real-world distribution power grid.

Several safety regulations particularly concerning the charging electric vehicles (EVs) are developed to ensure the electric safety and prevent the hazardous accidents, in which safety requirements for electric vehicle supply equipment (EVSE) and the EV battery are the two main driving factors. At present, quantitative assessment of electrical safety considering the operation conditions of large-scale electric vehicle charging stations (EVCSs) has still remained a challenge. Driven by the hierarchy of hazard control mechanisms, this paper proposes a holistic approach to evaluate the electrical safety of the large-scale EVCSs when coupled to renewable power generation. Our approach mainly focuses on several topics on the operational safety of EVCS primarily concerning: (1) the facility degradation which could potentially result in a compromised EVSE reliability performance and EVCS protection failure; (2) the cyber-attack challenges when the smart charging and the communication between EVCSs and electric utilities are enabled; (3) the potential mismatch between the renewable output and EVCS demand, which could trigger the system stability challenges during normal operation and inability to supply the critical EV loads during outages. The proposed framework will provide informative guidelines to the EVCS operators for continuous monitoring and effective management of the day-to-day EVCSs operation.

With the spatial flexibility exchange across the network, mobile energy storage systems (MESSs) offer promising opportunities to elevate power distribution system resilience against emergencies. Despite the remarkable growth in integration of renewable energy sources (RESs) in power distribution systems (PDSs), most recovery and restoration strategies do not unlock the full potential in such resources due to their inherent uncertainty and stochasticity. This paper develops a novel restoration mechanism in PDSs for routing and scheduling of MESSs integrated with stochastic RESs to achieve agile system response and recovery in facing the aftermath of high-impact low-probability (HILP) incidents. The proposed integrated model is presented as a non-convex non-linear stochastic optimization formulation with joint probabilistic constraints (JPCs). The problem is equivalently reformulated to a tractable mixed-integer linear programming (MILP) model that can be solved by commercial off-the-shelf solvers. Case studies on the IEEE 33-node and 123-node test systems demonstrate the effectiveness and scalability of the proposed framework in boosting the system resilience. This is achieved via effective routing and scheduling of MESSs jointly managed with dynamic network reconfiguration in presence of stochastic RESs.

The smart electricity grids have been evolving to a more complex cyber-physical ecosystem of infrastructures with integrated communication networks, new carbon-free sources of power generation, advanced monitoring and control systems, and a myriad of emerging modern physical hardware technologies. With the unprecedented complexity and heterogeneity in dynamic smart grid networks comes additional vulnerability to emerging threats such as cyber attacks. Rapid development and deployment of advanced network monitoring and communication systems on one hand, and the growing interdependence of the electric power grids to a multitude of lifeline critical infrastructures on the other, calls for holistic defense strategies to safeguard the power grids against cyber adversaries. In order to improve the resilience of the power grid against adversarial attacks and cyber intrusions, advancements should be sought on detection techniques, protection plans, and mitigation practices in all electricity generation, transmission, and distribution sectors. This survey discusses such major directions and recent advancements from a lens of different detection techniques, equipment protection plans, and mitigation strategies to enhance the energy delivery infrastructure resilience and operational endurance against cyber attacks. This undertaking is essential since even modest improvements in resilience of the power grid against cyber threats could lead to sizeable monetary savings and an enriched overall social welfare.

Mobile power sources (MPSs), including mobile emergency generators (MEGs), truck-mounted mobile energy storage systems (MESSs) and electric vehicles (EVs) have great potentials to be employed as grid-support resources during power grid emergency operating conditions to supply the critical loads and enhance the resilience of the distribution system (DS) via a swift disaster restoration. We here investigate the MPS dispatch (i.e., routing and scheduling) in coordination with DS dynamic network reconfiguration. We propose a two-stage restoration scheme to facilitate the DS restoration following the high-impact low-probability (HILP) seismic disasters. In the first stage, a seismic hazard is simulated through a Monte-Carlo simulation (MCS) engine to estimate the unavailability of power distribution branches under a suite of seismic force scenarios. In the second stage, a mixed-integer nonlinear programming (MINLP) optimization model is formulated for DS restoration that co-optimizes the routing and scheduling of MPSs and DS dynamic network reconfiguration. The MINLP model is then linearized to a mixed-integer linear programming (MILP) model to reduce the computation complexity, where the seismic-resilience recovery strategies are generated at different timescales. The efficacy of the proposed method is evaluated on the IEEE 33-node test system and the results verify a significant reduction in the load outages and an improved power system resilience to HILP earthquakes.