A Valuation Framework for Customers Impacted by Extreme Temperature-Related Outages
Min Gyung Yu, Monish Mukherjee, Shiva Poudela, Sadie R. Bender, Sarmad Hanif, Trevor D. Hardy, Hayden M. Reeve
TL;DR
This paper presents a comprehensive, customer-centric framework to value resilience against extreme temperature-related outages by coupling mortality and non-energy impacts with traditional interruption costs. It integrates a mortality-health-probability model, productivity and building-damage assessments, and monetary costs (including VSL and medical expenses) within a probabilistic, Monte Carlo framework. A scalable grid-simulation environment (TESP/GridLAB-D) models end-user thermal dynamics and distribution-grid response, demonstrated via a Texas Uri 2021 case with scenarios including base, controlled outage, and rolling outages with both damaged and hardened infrastructure. Key findings show significant reductions in mortality risk (up to 16%) and non-energy cost savings (up to 74%) when resilience enhancements and infrastructure hardening are applied, underscoring the framework’s value for decision-makers in resilience planning and risk management.
Abstract
Extreme temperature outages can lead to not just economic losses but also various non-energy impacts (NEI) due to significant degradation of indoor operating conditions caused by service disruptions. However, existing resilience assessment approaches lack specificity for extreme temperature conditions. They often overlook temperature-related mortality and neglect the customer characteristics and grid response in the calculation, despite the significant influence of these factors on NEI-related economic losses. This paper aims to address these gaps by introducing a comprehensive framework to estimate the impact of resilience enhancement not only on the direct economic losses incurred by customers but also on potential NEI, including mortality and the value of statistical life during extreme temperature-related outages. The proposed resilience valuation integrates customer characteristics and grid response variables based on a scalable grid simulation environment. This study adopts a holistic approach to quantify customer-oriented economic impacts, utilizing probabilistic loss scenarios that incorporate health-related factors and damage/loss models as a function of exposure for valuation. The proposed methodology is demonstrated through comparative resilient outage planning, using grid response models emulating a Texas weather zone during the 2021 winter storm Uri. The case study results show that enhanced outage planning with hardened infrastructure can improve the system resilience and thereby reduce the relative risk of mortality by 16% and save the total costs related to non-energy impacts by 74%. These findings underscore the efficacy of the framework by assessing the financial implications of each case, providing valuable insights for decision-makers and stakeholders involved in extreme-weather related resilience planning for risk management and mitigation strategies.