California Institute for Energy and Environment (CIEE)

In California and other states, air quality management districts are considering using energy efficiency and renewable energy to target both criteria pollutants (to meet air quality standards)and greenhouse gas emissions. This report presents a pilot study, designed to quantitativelymodel the emissions avoided through new energy efficiency/renewable energy programs not already required by state or federal statute. This work used California as a test case, producing an analysis that could theoretically satisfy U.S. Environmental Protection Agency requirements for using energy efficiency/renewable energy in State Implementation Plan compliance. Synapse Energy Economics used a Western Electricity Coordinating Council-scale production-cost simulation model (PROSYM) to examine the impact of incremental energy efficiency/renewable energy on generation and emissions of oxides of nitrogen (NOX), sulfur dioxide (SO2), and carbon dioxide. Sixteen energy efficiency/renewable energy scenarios were tested against a reference “base case,” representing the expected structure of Western Electricity Coordinating Council in 2016 under a full implementation of the California 33 percent renewable electricity standard and other energy statutes. The scenarios were comprised of four energy efficiency/renewable energy programs (solar, wind, and both baseload and peaking energy efficiency) enacted in four different California service territories (San Diego Gas and Electric, Southern California Edison, Los Angeles Department of Water and Power, and Pacific Gas and Electric). It was observed that in many cases, a large fraction of generation was displaced out-of-state, and total NOX and SO2 displaced in California tended to be small. Generation displaced out-of-state included coal resources, and therefore resulted in far larger emissions benefits in Western Electricity Coordinating Council regions other than California. The large range ofcriteria pollutant displacement from energy efficiency/renewable energy programs across California suggests that examining output variance and uncertainty is important, and that both model construct and input assumptions are key.