How Well Do Emission Factors Approximate Emission Changes from Electricity System Models?

Multiple forms of marginal and average emission factors have been developed to estimate the carbon emissions of adding technologies, such as electric vehicles or solar panels, to the electricity grid. Different methods can produce very different results and conclusions, indicating that choosing between methods is not trivial. Researchers would therefore like to know how well these emission factors can approximate emission changes in the actual power grid. This question remains unanswered because of the difficulty in characterizing the accuracy of these methods. Ideally, estimates would be compared to measured emission changes, but it is implausible to measure these changes on an actual grid. Instead, we propose testing these emission factor methods in a controlled environment, using an electricity system dispatch model as a reference for comparison. We find that average emission factors have lower accuracy when estimating emissions from demand shifts and observe the same for demand-based marginal emission factors at an hourly resolution. In contrast, incremental and thermal marginal emission factors can reproduce the emission changes of a power grid model under many testing conditions and scenarios. We also find that easier-to-use annual time averages offer similar results to finer time resolutions for marginal and average factors, except demand-based.

Heterogeneity in Economic and Carbon Benefits of Electric Technology Vehicles in the US.

To broadly contribute to sustainable mobility, electric technology vehicles (hybrid, electric, and plug-in-hybrid) must become more price competitive with internal combustion vehicles. This study assesses the economic and carbon benefits of electric technology vehicles in the U.S., accounting for household-by-household behavioral variability and geographical differences in fuel and electricity prices. This finer resolution provides insight into subsets of the population for whom adoption is economically or environmentally favorable, allowing us to construct marginal abatement cost curves for CO2 that account for geographic, behavioral, and stock heterogeneities. Currently, low gasoline prices and high initial expense means that, without subsidies, few consumers benefit financially from electric technology vehicles (1.7% of drivers). However, improved technology dramatically and nonlinearly increases both the number of consumers that benefit and corresponding carbon emissions that could be abated without government subsidy. Our results clarify cost targets that electric vehicle technology must achieve in order to deliver net financial and subsidy-free environmental benefits.

Estimating the Quantity of Wind and Solar Required To Displace Storage-Induced Emissions.

The variable and nondispatchable nature of wind and solar generation has been driving interest in energy storage as an enabling low-carbon technology that can help spur large-scale adoption of renewables. However, prior work has shown that adding energy storage alone for energy arbitrage in electricity systems across the U.S. routinely increases system emissions. While adding wind or solar reduces electricity system emissions, the emissions effect of both renewable generation and energy storage varies by location. In this work, we apply a marginal emissions approach to determine the net system CO2 emissions of colocated or electrically proximate wind/storage and solar/storage facilities across the U.S. and determine the amount of renewable energy required to offset the CO2 emissions resulting from operation of new energy storage. We find that it takes between 0.03 MW (Montana) and 4 MW (Michigan) of wind and between 0.25 MW (Alabama) and 17 MW (Michigan) of solar to offset the emissions from a 25 MW/100 MWh storage device, depending on location and operational mode. Systems with a realistic combination of renewables and storage will result in net emissions reductions compared with a grid without those systems, but the anticipated reductions are lower than a renewable-only addition.

Bulk energy storage increases United States electricity system emissions.

Bulk energy storage is generally considered an important contributor for the transition toward a more flexible and sustainable electricity system. Although economically valuable, storage is not fundamentally a "green" technology, leading to reductions in emissions. We model the economic and emissions effects of bulk energy storage providing an energy arbitrage service. We calculate the profits under two scenarios (perfect and imperfect information about future electricity prices), and estimate the effect of bulk storage on net emissions of CO2, SO2, and NOx for 20 eGRID subregions in the United States. We find that net system CO2 emissions resulting from storage operation are nontrivial when compared to the emissions from electricity generation, ranging from 104 to 407 kg/MWh of delivered energy depending on location, storage operation mode, and assumptions regarding carbon intensity. Net NOx emissions range from -0.16 (i.e., producing net savings) to 0.49 kg/MWh, and are generally small when compared to average generation-related emissions. Net SO2 emissions from storage operation range from -0.01 to 1.7 kg/MWh, depending on location and storage operation mode.

Inventory and transport of plastic debris in the Laurentian Great Lakes.

Plastic pollution in the world's oceans has received much attention, but there has been increasing concern about the high concentrations of plastic debris in the Laurentian Great Lakes. Using census data and methodologies used to study ocean debris we derive a first estimate of 9887 metric tonnes per year of plastic debris entering the Great Lakes. These estimates are translated into population-dependent particle inputs which are advected using currents from a hydrodynamic model to map the spatial distribution of plastic debris in the Great Lakes. Model results compare favorably with previously published sampling data. The samples are used to calibrate the model to derive surface microplastic mass estimates of 0.0211 metric tonnes in Lake Superior, 1.44 metric tonnes in Huron, and 4.41 metric tonnes in Erie. These results have many applications, including informing cleanup efforts, helping target pollution prevention, and understanding the inter-state or international flows of plastic pollution.