William R. Morrow

The Technology Path to Deep Greenhouse Gas Emissions Cuts by 2050: The Pivotal Role of Electricity

Electrifying Prospects Greenhouse gas emissions need to be reduced in order to decrease the risk of dangerous climate change, and a commonly advocated intermediate step to decarbonizing our energy production is to cut emissions by 80% by the year 2050. Williams et al. (p. 53, published online 24 November) analyze the infrastructure and technology requirements required to meet this goal in California and conclude that simply using the most technologically advanced types of energy supply now available will not be enough. Instead, transportation and other sectors will need to be converted largely to electrical systems, which would make decarbonized electricity the dominant form of energy supply. Such a transformation will require technologies that are not yet commercialized and intensive public-private and interindustry coordination at every stage of the process. Reducing greenhouse gas emissions to 80% below 1990 levels by 2050 requires widespread electrification of transportation and other sectors. Several states and countries have adopted targets for deep reductions in greenhouse gas emissions by 2050, but there has been little physically realistic modeling of the energy and economic transformations required. We analyzed the infrastructure and technology path required to meet California’s goal of an 80% reduction below 1990 levels, using detailed modeling of infrastructure stocks, resource constraints, and electricity system operability. We found that technically feasible levels of energy efficiency and decarbonized energy supply alone are not sufficient; widespread electrification of transportation and other sectors is required. Decarbonized electricity would become the dominant form of energy supply, posing challenges and opportunities for economic growth and climate policy. This transformation demands technologies that are not yet commercialized, as well as coordination of investment, technology development, and infrastructure deployment.

Life-cycle net energy assessment of large-scale hydrogen production via photoelectrochemical water splitting

Here we report a prospective life-cycle net energy assessment of a hypothetical large-scale photoelectrochemical (PEC) hydrogen production facility with energy output equivalent to 1 GW continuous annual average (1 GW HHV = 610 metric tons of H2 per day). We determine essential mass and energy flows based on fundamental principles, and use heuristic methods to conduct a preliminary engineering design of the facility. We then develop and apply a parametric model describing system-wide energy flows associated with the production, utilization, and decommissioning of the facility. Based on these flows, we calculate and interpret life-cycle net energy metrics for the facility. We find that under base-case conditions the energy payback time is 8.1 years, the energy return on energy invested (EROEI) is 1.7, and the life-cycle primary energy balance over the 40 years projected service life of the facility is +500 PJ. The most important model parameters affecting the net energy metrics are the solar-to-hydrogen (STH) conversion efficiency and the life span of the PEC cells; parameters associated with the balance of systems (BOS), including construction and operation of the liquid and gas handling infrastructure, play a much smaller role.

Assessment of Energy Efficiency Improvement and CO2 Emission Reduction Potentials in the Iron and Steel Industry in China

LBNL-XXXX E RNEST O RLANDO L AWRENCE B ERKELEY N ATIONAL L ABORATORY Assessment of Energy Efficiency Improvement and CO 2 Emission Reduction Potentials in the Iron and Steel Industry in China Ali Hasanbeigi, William Morrow, Jayant Sathaye, Eric Masanet, Tengfang Xu Energy Analysis and Environmental Impacts Department, Environmental Energy Technologies Division, Lawrence Berkeley National Laboratory, Berkeley, CA USA May 2012 This study is sponsored by Climate Economics Branch, Climate Change Division of U.S. Environmental Protection Agency, under Contract No. DE- AC02-05CH11231 with the U.S. Department of Energy.

Key Factors Influencing Autonomous Vehicles’ Energy and Environmental Outcome

Autonomous vehicles (AVs)—vehicles that operate without real-time human input—are a potentially disruptive technology. If widely adopted, there is the potential for significant impacts on the energy and environmental characteristics of the transportation sector. This paper provides an outline of key drivers likely to influence the magnitude and direction of these impacts. We identify three broad categories: vehicle characteristics, transportation network, and consumer choice. Optimistically, AVs could facilitate unprecedented levels of efficiency and radically reduce transportation sector energy and environmental impacts; on the other hand, consumer choices could result in a net increase in energy consumption and environmental impacts. As the technology matures and approaches market penetration, improved models of AV usage, especially consumer preferences, will facilitate the development of policies that promote reductions in energy consumption.

Volume 1: Survey of Available Information in Support of the Energy-Water Bandwidth Study of Desalination Systems:

Author(s): Rao, Prakash; Aghajanzadeh, Arian; Sheaffer, Paul; Morrow, William R.; Brueske, Sabine; Dollinger, Caroline; Price, Kevin; Sarker, Prateeti; Ward, Nicholas; Cresko, Joe

Electricity End Uses, Energy Efficiency, and Distributed Energy Resources Baseline

Author(s): Schwartz, Lisa; Wei, Max; Morrow, William; Deason, Jeff; Schiller, Steven R.; Leventis, Greg; Smith, Sarah; Leow, Woei Ling; Levin, Todd; Plotkin, Steven; Zhou, Yan

Lifecycle Industry GreenHouse gas, Technology and Energy through the Use Phase (LIGHTEnUP) – Analysis Tool User’s Guide

Author(s): Morrow, III, William; Shehabi, Arman; Smith, Sarah | Abstract: The LIGHTEnUP Analysis Tool (Lifecycle Industry GreenHouse gas, Technology and Energy through the Use Phase) has been developed for The United States Department of Energy’s (U.S. DOE) Advanced Manufacturing Office (AMO) to forecast both the manufacturing sector and product life-cycle energy consumption implications of manufactured products across the U.S. economy. The tool architecture incorporates publicly available historic and projection datasets of U.S. economy-wide energy use including manufacturing, buildings operations, electricity generation and transportation. The tool requires minimal inputs to define alternate scenarios to business-as-usual projection data. The tool is not an optimization or equilibrium model and therefore does not select technologies or deployment scenarios endogenously. Instead, inputs are developed exogenous to the tool by the user to reflect detailed engineering calculations, future targets and goals, or creative insights. The tool projects the scenario’s energy, CO2 emissions, and energy expenditure (i.e., economic spending to purchase energy) implications and provides documentation to communicate results. The tool provides a transparent and uniform system of comparing manufacturing and use-phase impacts of technologies. The tool allows the user to create multiple scenarios that can reflect a range of possible future outcomes. However, reasonable scenarios require careful attention to assumptions and details about the future. This tool is part of an emerging set of AMO’s life cycle analysis (LCA) tool such as the Material Flows the Industry (MFI) tool [1] [2], and the Additive Manufacturing LCA tool [3].

Unit Price Scaling Trends for Chemical Products

To facilitate early-stage life-cycle techno-economic modeling of emerging technologies, here we identify scaling relations between unit price and sales quantity for a variety of chemical products of three categories - metal salts, organic compounds, and solvents. We collect price quotations for lab-scale and bulk purchases of chemicals from both U.S. and Chinese suppliers. We apply a log-log linear regression model to estimate the price discount effect. Using the median discount factor of each category, one can infer bulk prices of products for which only lab-scale prices are available. We conduct out-of-sample tests showing that most of the price proxies deviate from their actual reference prices by a factor less than ten. We also apply the bootstrap method to determine if a sample median discount factor should be accepted for price approximation. We find that appropriate discount factors for metal salts and for solvents are both -0.56, while that for organic compounds is -0.67 and is less representative due to greater extent of product heterogeneity within this category.

Energy Efficiency Potential for China’s Cement Industry: A Bottom-Up Technology-Level Analysis

China’s annual cement production (i.e., 1,868 Mt) in 2010 accounted for nearly half of the world’s annual cement production in the same year. We analyzed 23 energy efficiency technologies and measures applicable to the processes in the cement industry. Using a bottom-up electricity conservation supply curve model, the cumulative cost-effective electricity savings potential for the Chinese cement industry for 2010-2030 is estimated to be 410 TWh, and the total technical electricity saving potential is 468 TWh. The fuel conservation supply curve model for the cement industry shows cumulative cost-effective fuel savings potential of 6,248 PJ-equivalent to the total technical potential.