An official website of the United States government
Sustained power outages are growing in scale and number primarily due to i) the increasing number and intensity of disasters and ii) decarbonization- and electrification-related grid changes. Outage mitigation technologies (e.g., backup diesel generators, and solar panels) increasingly provide vital electricity access during disasters. However, their adoption is inequitable due to individual- or community-level barriers and historic underinvestment in certain communities. We postulate that community-based Resilience Hubs (RHs), which are being increasingly deployed to provide on-site services during disasters, can be expanded to address this inequity by supplying backup power to vulnerable communities through islanded operations. To that end, we present Grid-Aware Tradeoff Analysis (GATA) framework to identify the best backup power systems for expanded RHs. To include technical, economic, and social facets in the framework, we will use three-phase power flow (TPF) and multi-criteria decision analysis (MCDA). TPF will enforce the electrical feasibility of islanded RH operation, and MCDA will quantify the economic, environmental, and equity-weighted outage mitigation performance. As a use case for GATA, we will evaluate multiple representative RHs in Richmond, California, and highlight the non-dominated systems for the electrically feasible RHs. We show the value of GATA's detailed grid simulation, its ability to quantify tradeoffs across scenarios, and its possible extensions.
more »
« less
Improving Power System Resilience through Decentralized Decision-Making
Natural disasters has been causing an increasing amount of economic losses in the past two decades. Natural disasters, such as hurricanes, winter storms, and wildfires, can cause severe damages to power systems, significantly impacting industrial, commercial, and residential activities, leading to not only economic losses but also inconveniences to people’s day-today life. Improving the resilience of power systems can lead to a reduced number of power outages during extreme events and is a critical goal in today’s power system operations. This paper presents a model for decentralized decision-making in power systems based on distributed optimization and implemented it on a modified RTS-96 test system, discusses the convergence of the problem, and compares the impact of decision-making mechanisms on power system resilience. Results show that a decentralized decision-making algorithm can significantly reduce power outages when part of the system is islanded during severe transmission contingencies.
more »
« less
- Award ID(s):
- 2131201
- PAR ID:
- 10388903
- Date Published:
- Journal Name:
- The 54th North American Power Symposium (NAPS)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract Critical infrastructure networks enable social behavior, economic productivity, and the way of life of communities. Disruptions to these cyber–physical–social networks highlight their importance. Recent disruptions caused by natural phenomena, including Hurricanes Harvey and Irma in 2017, have particularly demonstrated the importance of functioning electric power networks. Assessing the economic impact (EI) of electricity outages after a service disruption is a challenging task, particularly when interruption costs vary by the type of electric power use (e.g., residential, commercial, industrial). In contrast with most of the literature, this work proposes an approach to spatially evaluate EIs of disruptions to particular components of the electric power network, thus enabling resilience‐based preparedness planning from economic and community perspectives. Our contribution is a mix‐method approach that combines EI evaluation, component importance analysis, and GIS visualization for decision making. We integrate geographic information systems and an economic evaluation of sporadic electric power outages to provide a tool to assist with prioritizing restoration of power in commercial areas that have the largest impact. By making use of public data describing commercial market value, gross domestic product, and electric area distribution, this article proposes a method to evaluate the EI experienced by commercial districts. A geospatial visualization is presented to observe and compare the areas that are more vulnerable in terms of EI based on the areas covered by each distribution substation. Additionally, a heat map is developed to observe the behavior of disrupted substations to determine the important component exhibiting the highest EI. The proposed resilience analytics approach is applied to analyze outages of substations in the boroughs of New York City.more » « less
-
Abstract The ability to accurately measure recovery rate of infrastructure systems and communities impacted by disasters is vital to ensure effective response and resource allocation before, during, and after a disruption. However, a challenge in quantifying such measures resides in the lack of data as community recovery information is seldom recorded. To provide accurate community recovery measures, a hierarchical Bayesian kernel model (HBKM) is developed to predict the recovery rate of communities experiencing power outages during storms. The performance of the proposed method is evaluated using cross‐validation and compared with two models, the hierarchical Bayesian regression model and the Poisson generalized linear model. A case study focusing on the recovery of communities in Shelby County, Tennessee after severe storms between 2007 and 2017 is presented to illustrate the proposed approach. The predictive accuracy of the models is evaluated using the log‐likelihood and root mean squared error. The HBKM yields on average the highest out‐of‐sample predictive accuracy. This approach can help assess the recoverability of a community when data are scarce and inform decision making in the aftermath of a disaster. An illustrative example is presented demonstrating how accurate measures of community resilience can help reduce the cost of infrastructure restoration.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
-
Climate-induced extreme weather events, as well as other natural and human-caused disasters, have the potential to increase the duration and frequency of large power outages. Resilience, in the form of supplying a small amount of power to homes and communities, can mitigate outage consequences by sustaining critical electricity-dependent services. Public decisions about investing in resilience depend, in part, on how much residential customers value those critical services. Here we develop a method to estimate residential willingness-to-pay for back-up electricity services in the event of a large 10-day blackout during very cold winter weather, and then survey a sample of 483 residential customers across northeast USA using that method. Respondents were willing to pay US$1.7–2.3/kWh to sustain private demands and US$19–29/day to support their communities. Previous experience with long-duration outages and the framing of the cause of the outage (natural or human-caused) did not affect willingness-to-pay.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
