Enhancing the resilience of power distribution systems to extreme weather events is of critical concern. Upgrading the distribution system infrastructure by system hardening and investing in smart grid technologies effectively enhances grid resilience. Existing distribution system planning methods primarily consider the persistent cost of the expected events (such as faults and outages likely to occur) and aim at improving system reliability. The resilience to extreme weather events requires reducing the impacts of the high impact low probability (HILP) events that are characterized by the tail probability of the event impact distribution. Thus, the resilience-oriented system upgrades solutions need to be driven by the risks imposed by extreme weather events on the power grid infrastructure rather than persistent costs. This paper aims to develop a risk-based approach for the long-term resilience planning of active power distribution systems against extreme weather events. The proposed approach explicitly models (1) the impacts of HILP events using a two-stage risk-averse stochastic optimization framework, thus, explicitly incorporating the risks of HILP events in long-term planning decisions, and (2) the advanced distribution grid operations (in the aftermath of the event) such as intentional islanding into infrastructure planning problem. The inclusion of risk in the planning objective provides additional flexibility to the grid planners to analyze the trade-off between risk-neutral and risk-averse planning strategies.

With the increase in penetration of renewable energy sources (RES), traditional inertia estimation techniques based purely on the number of online synchronous generators are increasingly unsuitable, ultimately leading towards suboptimal frequency control in the electric power grid. The stochastic nature of RES additionally makes the system inertia a time-varying quantity. Furthermore, the frequency and inertial response of power systems change drastically in multiarea power systems with interconnected tie-lines. Hence, it is important for state/parameter estimation (e.g., inertia) in multiarea systems, while ensuring communication between each of the areas. In this article, a client–server-based federated learning framework is used to estimate power system inertia in a multiarea system. Federated learning is a machine learning technique where multiple decentralized devices are trained with local data, and a global model is updated and redistributed by a central server by aggregating the trained weights of the decentralized devices, without exchanging the local data. Using local frequency measurements, obtained from the phase-locked loop of an energy storage system, the inertia at each of the areas can be estimated locally via offline training using convolutional neural networks (CNNs), whereas the CNN weights update in an online fashion. The framework, tested on a two-area power system, accurately estimated the inertia constant for both independent and identically distributed (IID) and non-IID data. Furthermore, the CNN-based method outperformed conventional neural network-based estimation techniques in terms of number of communication rounds and estimation accuracy.

Extreme weather events have created a growing concern for power distribution systems, and it is challenging to justify planning budgets due to high levels of uncertainty in observing these events and their impacts on the system. This paper presents a trade-off analysis of different resilience-based active distribution system upgrade solutions, specifically to mitigate the outage risks of high-speed wind events. The resilience planning problem for unbalanced three-phase distribution systems is formulated as a two-stage risk-averse stochastic optimization model with conditional value-at-risk (CVaR) as the risk measure. The possible planning measures include installing grid-forming distributed generators (DGs) capable of islanded operation, installing remote-controlled tie-switches, and implementing line hardening solutions. Realistic wind data and fragility models are adopted to simulate high-speed wind event scenarios. The parameters used in this work for trade-off analysis are fragility models, risk preference, multiple planning resources, and planning budget allocation. The proposed approach is validated on a modified IEEE-123 bus test case. The analysis presented in this work can inform system operators about the trade-offs in optimal planning budget and resource allocation, ultimately enhancing the resilience of the distribution grid.

This paper presents sensitivity analyses of resilience-based active distribution system planning solutions with respect to different parameters. The distribution system planning problem is formulated as a two-stage risk-averse stochastic optimization model with conditional value-at-risk (CVaR) as the risk measure. The probabilistic scenarios are obtained using regional wind profiles, and Monte Carlo simulations are conducted to obtain failure scenarios based on component fragility models. The planning measure includes advanced distribution grid operations with intentional islanding measures. The three main parameters used in this work for sensitivity analysis are the number of scenarios, risk preference, and planning budget allocation. Such analysis can provide additional information to system operators on dispatching the planning budget and available resources properly to enhance the grid's resilience.

This paper introduces a probabilistic framework to quantify community vulnerability towards power losses due to extreme weather events. To analyze the impact of weather events on the power grid, the wind fields of historical hurricanes from 2000 to 2018 on the Texas coast are modeled using their available parameters, and probabilistic storm surge scenarios are constructed utilizing the hurricane characteristics. The vulnerability of hurricanes and storm surges is evaluated on a 2000 bus synthetic power grid model on the geographical footprint of Texas. The load losses, obtained via branch and substation outages, are then geographically represented at the county level and integrated with the publicly available Social Vulnerability Index to evaluate the Integrated Community Vulnerability Index (ICVI), which reflects the impacts of these extreme weather events on the socioeconomic and community power systems. The analysis concludes that the compounded impact of power outages due to extreme weather events can amplify the vulnerability of affected communities. Such analysis can help the system planners and operators make an informed decision.