Panel: Data-Driven Analysis for the Impact of Technology Adoption on Electricity Market Prices, Climate Mitigation and Energy Services
(Natural Resource, Energy, and Environmental Policy)

Saturday, November 10, 2018: 3:15 PM-4:45 PM
Taylor - Mezz Level (Marriott Wardman Park)

*Names in bold indicate Presenter

Panel Chairs:  Hongtao Yi, The Ohio State University
Discussants:  Yueming (Lucy) Qiu, University of Maryland, College Park and Ross C Beppler, Georgia Institute of Technology

Time-of-Use Pricing and the Environmental Impact of Electric Vehicle Charging Schedules
Debapriya Chakraborty, University of California, Irvine

Risky Business: Marginal Switching and Price Volatility in Pjm
Alessio Saretto, University of Texas, Dallas, Anastasia Shcherbakova, Texas A&M University and Jeremy Lin, DNV GL

Integration of clean energy technologies in electric grids affects various aspects of the system. Quantifying these impacts provide important information that can inform energy policymaking. The three papers in this panel offers lessons on the associated implications of technology adoption for electricity market prices, climate mitigation and energy services. It covers a diverse range of topics in the energy sector, including natural gas electricity generation, demand response and energy efficiency, and smart meters. Drawing on empirical data from PJM, MISO and the whole nation, papers in this panel improve our understanding of costs and benefits of clean energy technology adoptions.


The first paper in our panel examines whether natural gas generation leads to higher price volatility. The authors compare natural gas to coal, and use emergency outages of coal generators as a quasi-natural experiment to test the effect of natural gas generation on wholesale electricity price volatility. Based on PJM data from 2014 to 2016, the authors did not find significant increase in price or price volatility, given more use of natural gas in electricity generation.


The second paper examines the emission-reduction benefits of efficiency and demand response using marginal emission factors (MEFs) in high temporal and spatial resolution. The paper investigates the pattern in MEFs for CO2, NOx, and SO2 in MISO for hourly power generation. It asks if the MEFs correlate with locational marginal prices in the desirable way. Results suggest that MEFs are high in hours when prices are low. The estimates indicate that in the context of high real time LMPs, avoided emissions from demand response are lower than those from energy efficiency.


The third paper explores how increasing penetration of smart grid technology in the past decade in the United States has influenced other utility efforts for cleaner and smarter energy. The analysis draws on utility level data from EIA 861 form from 2007 to 2017 to test the relationship between smart meter penetration and utility development of a range of sustainable energy services. Analyses using aggregated macro level data suggest that utility smart meter rollouts tend to be positively correlated with the improvement of demand response and customer engagement experience, but not distributed generation. This analysis provides preliminary evidence for understanding the environmental and social benefits of AMI meters.