A Data-Driven Approach to Assessing Supply Inadequacy Risks Due to Climate-Induced Shifts in Electricity Demand

Area of Science:

• Climate Science
• Energy Systems Analysis
• Data Science

Background:

• The U.S. electric power system faces increasing vulnerability to extreme climate events.
• Climate-induced demand shifts and physical asset damage pose supply inadequacy risks.
• Unanticipated climate impacts on electricity demand require proactive risk assessment.

Purpose of the Study:

• To identify key risk factors for climate-sensitive electricity demand using a data-driven approach.
• To assess the vulnerability of the electricity sector to future climate variability and change.
• To propose tools for communicating climate-related inadequacy risks to stakeholders.

Main Methods:

• Leveraged advanced supervised learning theory to identify predictors of climate-sensitive demand.
• Analyzed residential, commercial, and industrial sectors.
• Conducted statistical sensitivity analysis to evaluate projected demand variability.

Main Results:

• Identified variations in mean dew point temperature as a common, major risk factor across all three sectors.
• Quantified the variability in projected electricity demand based on this key climate risk factor.
• Developed scenario-based heat maps for risk communication.

Conclusions:

• Mean dew point temperature is a critical factor influencing electricity demand under climate change.
• The proposed data-driven approach effectively identifies and communicates climate-related risks to the power sector.
• The methodology is applicable nationwide, using Ohio as a case study.