Abstract
In the face of atypical weather events, power infrastructure failures, and limited resources for resilience investment, energy decision-makers need data-driven metrics to allocate resilience investments and maximize the reduction of power outage impacts. For state-level planning, for instance, ranking the resilience of each county is key to ensuring effective distribution of resources. In such cases, resilience for each spatial unit is multifaceted and is captured by a set of indicators (i.e., metrics) that can be combined into an overall score that reduces the complexity of power outage dynamics to a single decision metric. However, weighting of these indicators is often addressed by simplifying assumptions (i.e., equal weights) or semi-subjective methods that rely on user-defined weights that can introduce biases (e.g., weighted average score). Within the disaster risk reduction and resilience engineering community, a recurring challenge in multicriteria decision-making is the objective weighting of indicators for composite indices. To address this issue, we have leveraged a Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) combined with an entropy-based weighting approach to calculated the integrated scores. This method objectively determines the importance of each metric, better discerns between spatial units (i.e., counties), and offers a more reliable ranking of counties according to their relative resilience attributes. By improving methods for integrating resilience indicators, our approach helps planners and decision-makers prioritize resources more effectively for more efficient resilience investments.
| Original language | English |
|---|---|
| Pages (from-to) | 205439-205456 |
| Number of pages | 18 |
| Journal | IEEE Access |
| Volume | 13 |
| DOIs | |
| State | Published - 2025 |
Funding
EAGLE-I data used in this work are based on work supported by the DOE Office of Cybersecurity, Energy, Security, and Emergency Response. The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paid-up, irrevocable, and worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for the U.S. Government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (https://www.energy.gov/doe-public-access-plan). EAGLE-I data used in this work are based on work supported by the DOE Office of Cybersecurity, Energy, Security, and Emergency Response. ODIN (Outage Data Initiative Nationwide) is a standardized power-outage data-sharing network supported by the U.S. Department of Energy’s Office of Electricity.
Keywords
- Entropy method
- multicriteria decision making (MCDM)
- power outages
- ranking
- resilience
- technique for order of preference by similarity to ideal solution (TOPSIS)
- weighted average