You are here

Navigating the Clean Energy Transition in a Changing Climate

Vertical Tabs

Description

Session Description: 

Providing reliable, affordable, and low-carbon electricity to customers, while accelerating the adoption of low-carbon technologies, such as renewable energy, electric vehicles, and electrified buildings, requires ongoing ingenuity and innovations. On top of these planning and procurement challenges, climate change has exposed system-wide vulnerabilities and elevated the urgency of adapting the electric sector to new risks. To do this, electric sector planners face the difficult task of understanding which climate science and technology adoption data are appropriate to use, and how best to apply that data to plan for an uncertain future.

The problem of identifying and applying appropriate climate data, which has been referred to as “The Practitioner’s Dilemma,” is not new, and it affects multiple U.S. economic sectors beyond electricity (e.g., agriculture, drinking water). For the electric sector, it is becoming more pressing as the sector experiences climate impacts with increasing frequency and severity. Severe storms, heat waves, droughts, wildfires and other extreme weather events are causing electricity outages, higher electricity costs, economic losses, environmental damage, and human mortality/morbidity—often in the most socioeconomically disadvantaged areas. For example, to address climate change impacts, electric utilities need to consider operational changes as well as long-term, infrastructure investments. These decisions span transmission and distribution planning, from bulk system reliability and resiliency, to customer-level investments in electric vehicles, rooftop solar, energy storage, and fuel-switching for space and water heating.

This set of workshops is the second step in a project co-convened with the Science for Climate Action Network (SCAN) intended to take stock of ongoing efforts to produce decision-relevant climate information, evaluate fitness of climate information, and characterize uncertainty that facilitates implementation of adaptation and mitigation measures. It seeks to promote collaborative learning and a more systematic approach to assessment of climate science for applications.