Measuring the economic and societal value of reliability/resilience investments: case studies of islanded communities

ABSTRACT

Large-scale disasters have exposed vulnerabilities in energy systems and interdependent infrastructure, underscoring the importance of proactively mitigating risks to critical infrastructure. This paper focuses on strengthening power system planning by incorporating the costs associated with prolonged and extensive power interruptions to bolster resilience. To achieve this, duration-dependent customer damage functions are developed, capturing the direct, indirect, and societal impacts of power interruptions, ranging from short, localized events to widespread and long-duration ones. The research methodology is applied to three islanded communities facing substantial resilience challenges with limited data availability. Three customer interruption cost surveys are conducted with local electricity customers, yielding valuable insights into duration-dependent customer damage functions for direct, indirect, and monetizable societal costs. Significantly, the power interruption cost estimates derived from these functions vary considerably from those in the contiguous U.S. and even between the different islanded communities, reflecting their distinct hazard profiles and geographical characteristics. In conclusion, this study demonstrates the potential of duration-dependent customer damage functions to enhance power system resilience. It also identifies several areas for further research, paving the way for a more robust and resilient power infrastructure.

KEYWORDS:

• Electric power resilience
• value of resilience
• duration-dependent customer damage function
• power outages
• power system planning

Abbreviations

ANL	=	Argonne National Laboratory
BCA	=	Benefit-Cost Analysis
BEA	=	Bureau of Economic Analysis
CDF	=	Customer Damage Function
CIC	=	Customer Interruption Cost
GDP	=	Gross Domestic Product
EPA	=	Environmental Protection Agency
FEMA	=	Federal Emergency Management Agency
FRONTIER	=	Framework for Overcoming Natural Threats to Islanded Energy Resilience
ICE Calculator	=	Interruption Cost Estimate Calculator
LBNL	=	Lawrence Berkeley National Laboratory
LDWI	=	Long-Duration Widespread power Interruptions
LNR	=	Large non-residential
MBDC	=	Multiple-Bounded Discrete Choice
NREL	=	National Renewable Energy Laboratory
O&M	=	Operation and Maintenance
RIMS II	=	Regional Input-output Modeling Systems (Type) II
SMNR	=	Small and medium non-residential
VOLL	=	Value of Lost Load
WTP	=	Willingness-to-pay

Acknowledgments

We would like to acknowledge the staff at the three electric utilities who provided support throughout the design and implementation of the surveys. We would also like to thank Jennifer Schmidt, Jessica Passini, John Rivera, and Forrest Chargualaf for their help with the administration of the surveys. We would like to thank Reviewers for taking the necessary time and effort to review the manuscript. Finally, we sincerely thank the survey respondents for taking the time to complete these surveys. The U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy (EERE) provided the funding which made this research project possible.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The (anonymized) data supporting this study’s findings are available upon request from the authors.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/23789689.2023.2280870.

Notes

1. See Baik et al. (2021) and Gorman (2022) for the methodologies that are used to estimate power interruption costs.

2. The ICE calculator can be accessed at: https://icecalculator.com/home

3. Stated preference studies have several inherent issues, including hypothetical bias. However, a study that is carefully designed and properly conducted is assumed to be able to provide useful input into decision making processes. Under this assumption, we adopted the stated preference method to estimate the WTP or cost/saving estimates while making sure that we followed the suggested guidelines (Arrow, Solow, Portney, et al., 1993).

4. The most widely-used techniques under this approach include bidding games, payment card method, open-ended questions, dichotomous choice, and multiple-bounded discrete choice (MBDC) method. See Baik, Davis, and Morgan (2018) for the review of major elicitation techniques and their pros and cons. Among these methods, we selected open-ended questions (for non-residential surveys) and MBDC (for residential surveys) to fit the characteristics of power interruption costs of different customer segments, the number of questions needed to be asked to elicit the costs, and difficulties in answering the questions.

5. We obfuscated the names of the utilities in this study to emphasize the application of the generalized framework and de-emphasize utility-specific circumstances affecting the resilience of the power system.

6. We had the fourth scenario of a one-month-long power interruption scenario as the worst-possible scenario for two of our study regions: Southeast and Interior Alaska and the U.S. territory in the South Pacific. However, the respondents struggled to understand the scenario and infer the economic impacts. Furthermore, societal impacts were hard to estimate as the given situations were too extreme, and the impacts of emergency aid and relief from external organizations are hard to predict. To ensure the reliability of the power interruption cost estimates, we decided to drop the fourth scenario.

7. FEMA’s BCA Tool can be found here: https://www.fema.gov/grants/tools/benefit-cost-analysis

8. Operating reliability is defined by the North American Electric Reliability Corporation (NERC) as ‘(the power system’s) ability to withstand sudden disturbances while avoiding uncontrolled cascading blackouts or damage to equipment’ (North American Electric Reliability Corporation NERC, 2020).

9. Resource adequacy is defined as ‘(the power system’s) ability to supply the aggregate electrical demand and energy requirements of the electricity customers at all times’ (North American Electric Reliability Corporation NERC, 2020).

10. FRONTIER can be accessed at:https://frontier.lbl.gov.

Additional information

Funding

U.S. Department of Energy Office of Energy Efficiency and Renewable Energy under Contract No. DE-AC02-05CH11231.

Notes on contributors

Sunhee Baik

Sunhee Baik is a Research Scientist in the Energy Markets and Policy Department at Lawrence Berkeley National Laboratory (LBNL). Her interests focus on policy problems where technology, science, and public perceptions play a central role. Her research focuses on characterizing energy system vulnerabilities from natural disasters and emerging threats, obtaining economic and social costs of power outages, and enhancing resilience. Sunhee holds a Ph.D. in Engineering and Public Policy from Carnegie Mellon University (CMU), an M.S. in Industrial and System Engineering from Korea Advanced Institute of Science and Technology (KAIST), and a B.A. in Industrial Engineering from Yonsei University.

Nichole L. Hanus

Nichole L. Hanus is a Research Scientist in the Energy Markets and Policy Department at LBNL. Nichole conducts research aimed at improving electricity grid resilience and reliability. Her work is informed by her background in mechanical engineering, behavioral decision sciences, and public policy. Prior to joining LBNL, Nichole worked as an Energy Engineer at Sieben Energy Associates in Chicago and as a Consultant at E3 in San Francisco. Nichole holds a Ph.D. and M.S. in Engineering and Public Policy from Carnegie Mellon University, as well as a B.S. in Mechanical Engineering from the University of Dayton.

Juan Pablo Carvallo

Juan Pablo Carvallo is a Research Scientist in the Energy Markets and Policy Department at LBNL. Research areas at the lab focus on long-term power system planning, integration and planning of distributed energy resources and electric vehicles, and reliability and resilience valuation. Dr. Carvallo holds Ph.D. and M.S. degrees in Energy and Resources from the University of California, Berkeley, as well as P.E. and B.S. degrees in Electronics Engineering from Universidad Técnica Federico Santa Maria, Chile.

Peter H. Larsen

Peter Larsen is a Staff Scientist and Leader of the Energy Markets and Policy Department at the Lawrence Berkeley National Laboratory. Peter conducts research and analysis on electricity reliability and resilience, energy efficiency, and regional electric system planning. Peter holds a Ph.D. in Management Science and Engineering from Stanford University; M.S. degrees from Stanford University (Management Science and Engineering) and Cornell University (Natural Resource Economics); and a B.A. in Economics from the University of Montana at Missoula.