An official website of the United States government
In recent decades, blackouts have shown an increasing prevalence of power outages due to extreme weather events such as hurricanes. Precisely assessing the spatiotemporal outages in distribution networks, the most vulnerable part of power systems, is critical to enhancing power system resilience. The Sequential Monte Carlo (SMC) simulation method is widely used for spatiotemporal risk analysis of power systems during extreme weather hazards. However, it is found here that the SMC method can lead to large errors as it repeatedly samples the failure probability from the time-invariant fragility functions of system components in time-series analysis, particularly overestimating damages under evolving hazards with high-frequency sampling. To address this issue, a novel hazard resistance-based spatiotemporal risk analysis (HRSRA) method is proposed. This method converts the failure probability of a component into a hazard resistance and uses it as a time-invariant value in time-series analysis. The proposed HRSRA provides an adaptive framework for incorporating high-spatiotemporal-resolution meteorology models into power outage simulations. By leveraging the geographic information system data of the power system and a physics-based hurricane wind field model, the superiority of the proposed method is validated using real-world time-series power outage data from Puerto Rico, including data collected during Hurricane Fiona in 2022.
more »
« less
This content will become publicly available on May 21, 2027
An Analytical Model for Spatiotemporal Risk of Grid Infrastructure Under Extreme Events
Accurately quantifying the spatiotemporal risks to power grid infrastructure under extreme events is critical to ensuring system stability and resilience. The recently proposed hazard resistance-based spatiotemporal risk analysis (HRSRA) model addresses a key limitation of the widely used Sequential Monte Carlo method, which tends to overestimate infrastructure damage risk due to repeated sampling in time-series analysis. However, the HRSRA method relies on extensive simulations. This letter proposes an analytical framework and derives closed-form expressions for key statistical metrics to quantify grid infrastructure risk during extreme events. Bypassing the numerical simulations of HRSRA, the proposed analytical method agrees well with power outage observations in Puerto Rico.
more »
« less
- Award ID(s):
- 2103754
- PAR ID:
- 10694013
- Publisher / Repository:
- Institute of Electrical and Electronics Engineers (IEEE)
- Date Published:
- Journal Name:
- IEEE Transactions on Power Systems
- ISSN:
- 0885-8950
- Page Range / eLocation ID:
- 1 to 4
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
ABSTRACT With the increasing frequency of natural disasters, operators must prioritize improvements in the existing electric power grid infrastructure to enhance its resilience. Resilience against extreme weather events requires the grid planners to account for high‐impact, low‐probability (HILP) events during the planning stage. This paper proposes a resilience‐driven two‐stage stochastic planning model that jointly optimizes proactive generation dispatch, line hardening, line capacity upgrade and distributed generation sizing and siting decisions to minimize both load sheding and its risk for extreme weather scenarios. The risk is modelled using conditional value‐at‐risk (CVaR), and a representative scenario sampling method is used to alleviate computational complexity without sacrificing solution quality over a wide range of scenarios. The overall framework is tested on a standard IEEE reliability test system to evaluate the effectiveness of the proposed approach and also validated using out‐of‐sample scenarios. Several planning portfolios are presented that can help system planners identify trade‐offs between system resilience, planning budget, and risk aversion and prioritize the investments. The results suggest that line hardening is the most effective resilience‐enhancing investment under limited budgets, while the combination of line hardening and backup DGs minimizes the risk further as the planning budget increases.more » « less
-
Wildfires pose a growing risk to public safety in regions like the western United States, and, historically, electric power systems have ignited some of the most destructive wildfires. To reduce wildfire ignition risks, power system operators preemptively de-energize high-risk power lines during extreme wildfire conditions as part of "Public Safety Power Shutoff" (PSPS) events. While capable of substantially reducing acute wildfire risks, PSPS events can also result in significant amounts of load shedding as the partially de-energized system may not be able to supply all customer demands. In this work, we investigate the extent to which infrastructure investments can support system operations during PSPS events by enabling reduced load shedding and wildfire ignition risk. We consider the installation of grid-scale batteries, solar PV, and line hardening or maintenance measures (e.g., undergrounding or increased vegetation management). Optimally selecting the locations, types, and sizes of these infrastructure investments requires considering the line de-energizations associated with PSPS events. Accordingly, this paper proposes a multi-period optimization formulation that locates and sizes infrastructure investments while simultaneously choosing line de-energizations to minimize wildfire ignition risk and load shedding. The proposed formulation is evaluated using two geolocated test cases along with realistic infrastructure investment parameters and actual wildfire risk data from the US Geological Survey. We evaluate the performance of investment choices by simulating de-energization decisions for the entire 2021 wildfire season with optimized infrastructure placements. With investment decisions varying significantly for different test cases, budgets, and operator priorities, the numerical results demonstrate the proposed formulation's value in tailoring investment choices to different settings.more » « less
-
Climate change is expected to intensify the effects of extreme weather events on power systems and increase the frequency of severe power outages. The large-scale integration of environment-dependent renewables during energy decarbonization could induce increased uncertainty in the supply–demand balance and climate vulnerability of power grids. This Perspective discusses the superimposed risks of climate change, extreme weather events and renewable energy integration, which collectively affect power system resilience. Insights drawn from large-scale spatiotemporal data on historical US power outages induced by tropical cyclones illustrate the vital role of grid inertia and system flexibility in maintaining the balance between supply and demand, thereby preventing catastrophic cascading failures. Alarmingly, the future projections under diverse emission pathways signal that climate hazards — especially tropical cyclones and heatwaves — are intensifying and can cause even greater impacts on the power grids. High-penetration renewable power systems under climate change may face escalating challenges, including more severe infrastructure damage, lower grid inertia and flexibility, and longer post-event recovery. Towards a net-zero future, this Perspective then explores approaches for harnessing the inherent potential of distributed renewables for climate resilience through forming microgrids, aligned with holistic technical solutions such as grid-forming inverters, distributed energy storage, cross-sector interoperability, distributed optimization and climate–energy integrated modelling.more » « less
-
Distribution networks, with large-scale integration of distributed renewable resources, particularly rooftop solar photovoltaic systems, represent the most extensive yet vulnerable components of modern electric power systems during climate extremes such as hurricanes. However, existing day-ahead electricity dispatch approaches primarily focus on the transmission network and lack the capability to manage the spatiotemporal risks associated with the vast distribution networks, which can potentially lead to significant power imbalances due to the mismatches between scheduled generation and actual demand. To address this increasingly critical gap under intensifying climate extremes and growing distributed renewable integration, we introduce Risk-aware Electricity Dispatch under Climate Extremes with Renewable integration (REDUCER), a risk-aware day-ahead electricity dispatch model that incorporates high-resolution spatiotemporal risk analysis for distribution networks with large-scale distributed renewable integration into an Entropic Value-at-Risk-constrained mixed-integer convex optimization framework. Applied to the 2022 Puerto Rico power grid under Hurricane Fiona, the proposed REDUCER model is seen to effectively manage these risks with substantially less reliance on additional flexibility resources to cope with power imbalances, reducing overall operational costs by about 30% under extreme cases compared to standard unit commitment strategies already informed by average demand loss. Also, the proposed REDUCER model consistently demonstrates its effectiveness in managing the increasing temporal net demand variability introduced by growing large-scale distributed solar integration while maintaining minimal operational costs. This model offers a practical solution for cost-effective and resilient electricity dispatch of modern power systems with large-scale renewable integration facing intensifying climate risks.more » « less
