Data-driven approaches to built environment flood resilience: A scientometric and critical review

Rathnasiri, Pavithra; Adeniyi, Onaopepo; Thurairajah, Niraj

doi:10.1016/j.aei.2023.102085

Cited by 13 publications

(1 citation statement)

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“…Assessing the impacts of flooding on power system operations requires having high-resolution space-time estimates of flooding depth. In this study, we use a purely data-driven approach [44], following a similar assimilative approach to [45,46], integrating different types of data to achieve flooding estimates (as opposed to physical hydrologic models). We start with records of 'high water marks' (maximum flooding depth) that were collected during Hurricane Florence from [47] (see blue dots in figure 3(b)).…”

Section: Flooding Analysismentioning

confidence: 99%

High-resolution, open-source modeling of inland flooding impacts on the North Carolina bulk electric power grid

Prieto-Miranda,

Kern

2024

Environ. Res.: Energy

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Although damages to local distribution systems from wind and fallen trees are typically responsible for the largest fraction of electricity outages during hurricanes, outages caused by flooding of electrical substations pose a unique risk. Electrical substations are a key component of electric power systems, and in some areas, the loss of a single substation can cause widespread power outages. Before repairing damaged substations, utilities must first allow floodwaters to recede, potentially leaving some customers without power for weeks following storms. As economic losses from flooding continue to increase in the U.S., there has been increasing attention paid to the potential impacts of flooding on power systems. Yet, this attention has mostly been limited to geospatial risk assessments that identify what assets are in the path of flooding. Here, we present the first major attempt to understand how flooding from hurricanes and other extreme precipitation events affects the dynamic behavior of power networks, including losses of demand and generation, and altered power flows through transmission lines. We use North Carolina, hit by major hurricanes in three of the past seven years, as a test case. Using open-source data of grid infrastructure, we develop a high-resolution direct current optimal power flow model that simulates electricity production and generators and power flows through a network consisting of 662 nodes and 790 lines. We then simulate grid operations during the historical (2018) storm Hurricane Florence. Time series of flooding depth at a discrete set of “high water” mark points from the storm are used to spatially interpolate flooding depth across the footprint area of the storms on an hourly basis. Outages of substations and solar farms due to flooding are translated to location-specific losses of demand and solar power production throughout the network. We perform sensitivity analysis to explore grid impacts as a function of the height of sensitive equipment at substations. Results shed light on the potential for localized impacts from flooding to have wider impacts throughout the grid (including in areas not affected by flooding), with performance tracked in terms of transmission line flows/congestion, generation outputs, and customer outages.

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Section: Flooding Analysismentioning

confidence: 99%