Marginal Emissions Factors for Electricity Generation in the Midcontinent ISO

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.

Current and Future Estimates of Marginal Emission Factors for Indian Power Generation

Emission factors from Indian electricity remain poorly characterized, despite known spatial and temporal variability. Limited publicly available emissions and generation data at sufficient detail make it difficult to understand the consequences of emissions to climate change and air pollution, potentially missing cost-effective policy designs for the world's third largest power grid. We use reduced-form and full-form power dispatch models to quantify current (2017-2018) and future (2030-2031) marginal CO₂, SO₂, NO_X, and PM_2.5 emission factors from Indian power generation. These marginal emissions represent emissions changes due to small changes in demand. For 2017-2018, spatial variability in marginal CO₂ emission factors range 3 orders of magnitude across India's states. There is limited seasonal and intraday variability with coal generation likely to meet changes in demand more than half the time in more than half of the states. Assuming the Government of India approximate 2030 targets, the median marginal CO₂ emission factor across states decreases by approximately a factor of 2, but emission factors still span 3 orders of magnitude across states. Under 2030-2031 assumptions there is greater seasonal and intraday variability by up to factors of two and four, respectively. Estimates provide emission factors to evaluate interventions such as electric vehicles, increased air conditioning, and energy efficiency.

Health benefits from cleaner vehicles and increased active transportation in Seattle, Washington

BACKGROUND

Climate mitigation policies that focus on the transportation sector yield near-term health co-benefits that could motivate policy action.

OBJECTIVE

We quantified CO₂ emission reductions as well as the air pollution and health benefits of urban transportation policies promoting electric vehicles (EV) and walking and bicycling in Seattle, Washington.

METHODS

We compared a business-as-usual scenario projected to 2035 with intervention scenarios in which 35% of gasoline vehicles were switched to EV, and 50% of car trips less than 8 kilometers were replaced by walking or bicycling. We modeled changes in primary traffic-generated oxides of nitrogen (NO_x) and fine particulate matter (PM_2.5) as well as walking and bicycling activity, CO₂ emissions from traffic, and fatal traffic injuries due to the transportation policy scenarios. We estimated the impacts of these changes on annual cases of asthma and premature mortality in the Seattle population.

RESULTS

Increasing the use of EV, walking, and bicycling is estimated to reduce CO₂ emissions by 744 tons/year (30%) and lower annual average concentrations of primary traffic-generated NO_x and PM_2.5 by 0.32 ppb (13%) and 0.08 μg/m³ (19%), respectively. In Seattle, the lower air pollutant concentrations, greater active transportation, and lower fatal traffic injuries would prevent 13 (95% CI: -1, 28), 49 (95% CI: 19, 71), and 5 (95% CI: 0, 14) premature deaths per year, respectively and 20 (95% CI: 8, 27) cases of asthma per year.

SIGNIFICANCE

Moving towards cleaner vehicles and active transportation can reduce CO₂ emissions, improve air quality, and population health. The resulting public health benefits provide important motivation for urban climate action plans.

IMPACT STATEMENT

Using key components of the health impact assessment framework, we quantify the environmental and health benefits of urban transportation policy scenarios that promote electric vehicle use and replace short car trips with walking and bicycling as compared with a business as usual scenario in 2035. Our findings demonstrate that transportation scenarios promoting cleaner vehicles and active transportation can reduce CO₂ emissions, improve air quality, and increase physical activity levels, resulting in significant public health benefits.

Estimation of Emission Factors from Purchased Electricity for European Countries: Impacts on Emission Reduction of Electricity Storage

To evaluate the reduction brought about by energy storage technology, it is essential to first have accurate data on carbon emissions from electricity consumption. However, when gathering this data by evaluating marginal emission factors (MEFs), previous research measured only generation emissions and direct transfer emissions while ignoring the impact of embodied emissions from the cross-grid transfer. To gather more accurate data, this study constructs an electricity network composed of 28 European countries in 2019 and compares the difference between the MEFs when considering the network-wide emissions and the MEFs when only considering generation emissions and direct transfer emissions for electricity trade (neglecting the indirect emissions in purchased electricity). Three energy storage strategies are adopted to evaluate the carbon emission reduction benefits of energy storage. The results show that the errors in emission accounting and MEF calculation are 7% and 10%, respectively, if the impact of electricity trade is not taken into account. When disregarding the indirect emissions from electricity trade, the errors in emission accounting and MEF calculation are 1%. Implementing wind curtailment reduction strategies for energy storage systems could effectively reduce electricity carbon emissions, more than 200 gCO₂/kWh in most countries with 100% storage efficiency. The accuracy of MEFs has a significant impact on the results of energy storage benefits, and the choice of storage strategies has different effects on electricity emissions in the same country. Our methods have general applicability for other regions and countries.

Planning for the evolution of the electric grid with a long-run marginal emission rate

Emissions factors are widely used to estimate how various interventions would influence emissions from the electric sector. Both of the most commonly used metrics, however, neglect how changes in electricity demand can influence the structural evolution of the grid (the building and retiring of capital assets, such as generators). This omission can be significant when the factors are intended to comprehensively reflect the consequences of an intervention. In this work we evaluate a lesser known metric—the long-run marginal emission rate (LRMER)—which incorporates both the operational and structural implication of changes in electricity demand. We apply a modeling framework to compare the LRMER to the two near-ubiquitous metrics, and show that the LRMER can outperform the other two metrics at anticipating the emissions induced by a range of interventions. This suggests that adopting the LRMER could improve decision-making, particularly by better capturing the projected role of renewable generators in the evolution of the power sector.

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A long-run marginal emission rate captures both operational and structural impacts

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A LRMER was compared against short-run marginal and average emission rates

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The LRMER outperformed both the SRMER and AER at estimating emission impacts

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Integrating SRMER across an intervention’s lifetime may not describe its total impact

Lifecycle cost and carbon implications of residential solar-plus-storage in California

Capacities of residential photovoltaics (PV) and battery storage are rapidly growing, while their lifecycle cost and carbon implications are not well understood. Here, we integrate PV generation and load data for households in California to assess the current and future lifecycle cost and carbon emissions of solar-plus-storage systems. Our results show that installing PV reduces $180-$730 and 110-570 kgCO₂ per year per household in 2020. However, compared to solar-only system, adding battery storage increases lifecycle costs by 39%-67%, while impact on emissions is mixed (-20% to 24%) depending on tariff structure and marginal emission factors. In 2040, under current decarbonization and cost trajectories, solar-plus-storage leads to up to 31% higher lifecycle costs and up to 32% higher emissions than solar-only systems. Designing a tariff structure with wider rate spreads aligned with marginal carbon emissions, and reducing the costs and embodied emissions of batteries are crucial for broader adoption of low-carbon residential solar-plus-storage.

Reducing the life cycle environmental impact of electric vehicles through emissions-responsive charging

Electric vehicles (EVs) are currently being promoted to reduce transport emissions. We present a life cycle assessment of EV charging behaviors based on marginal emissions factors. For Great Britain, we find that electricity consumption accounts for the highest proportion of life cycle carbon emissions from EVs. We highlight the potential life cycle carbon emissions reduction brought by charging during periods when the grid mix produces relatively low emissions. While our study focuses on Great Britain, we have applied our methodology to several European countries with contrasting electricity generation mixes. Our analysis demonstrates that countries with a high proportion of fossil energy will have reduced benefits from deploying EVs, but are likely to achieve increased benefits from smart charging approaches. We conclude that using marginal emissions factors is essential to understanding the greenhouse gas impacts of EV deployment, and that smart charging tied to instantaneous grid emissions factors can bring benefits.

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Propose LCA methodology for EV charging behaviors

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Marginal emissions factors are used to improve LCA accuracy

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Smart charging could reduce emissions by up to 6% for certain charging event

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Plug-in duration is found to have a crucial impact on smart charging performance

Comparison between Historical and Real-Time Techniques for Estimating Marginal Emissions Attributed to Electricity Generation

Electricity generation is tied to various environmental and social consequences. In prior studies, the environmental emissions associated with electricity generation were calculated using average emission factors (AEFs) whose use is different from the method of marginal emission factors (MEFs) in regard to the geographical redefinition and new policies applied to the US electricity grid in 2013. Moreover, the amount of emissions being released at a generation site depends on the technology of the generating units; it is important to take into account this factor as well. Thus, this paper provides comparisons between different historical and real-time approaches of estimating MEFs (i.e., CO2, SO2, and NOx) for the Midcontinent Independent System Operator (MISO) electricity region. The region under study is the same for all the scenarios, although the comparative time frames are different. The study is focused on the similarities observed in the data trends and system behaviors. We carry out different temporal comparisons whose results show the value of real-time approaches for estimating the MEFs for each location and at any time. These approaches can be extended to other regions to assist with proper investment and policy making, thereby increasing the grid efficiency, mitigating the environmental emissions, and clarifying the byproducts of energy consumption.

Accounting for the Greenhouse Gas Emission Intensity of Regional Electricity Transfers

Accurately quantifying greenhouse gas (GHG) emissions is essential for climate policy implementation but challenging in the case of electricity transfers across regulatory jurisdictions. Regulating emissions associated with delivered electricity is further complicated by contractual arrangements for dynamic electricity transfers that confound emission accounting approaches rooted in the physics of grid operations. Here, we propose a novel consumption-based accounting methodology to reconcile the nominal and the physical flows of electricity from generators to consumers. We also compare capacity factor-based and regression-based approaches for estimating default emission factors, in the absence of fully specified nominal electricity flows. As a case study, we apply this approach to assess the methods by which California regulators quantify specified and unspecified electricity imports and their associated GHG emissions. Collectively, these efforts illustrate principles for a comprehensive, empirical accounting framework that could inform efforts to improve the accuracy and consistency of policies regulating regional electricity transfers.

Framework for Accounting Reference Levels for REDD+ in Tropical Forests: Case Study from Xishuangbanna, China

The United Nations’ expanded program for Reducing Emissions from Deforestation and Forest Degradation (REDD+) aims to mobilize capital from developed countries in order to reduce emissions from these sources while enhancing the removal of greenhouse gases (GHGs) by forests. To achieve this goal, an agreement between the Parties on reference levels (RLs) is critical. RLs have profound implications for the effectiveness of the program, its cost efficiency, and the distribution of REDD+ financing among countries. In this paper, we introduce a methodological framework for setting RLs for REDD+ applications in tropical forests in Xishuangbanna, China, by coupling the Good Practice Guidance on Land Use, Land Use Change, and Forestry of the Intergovernmental Panel on Climate Change and land use scenario modeling. We used two methods to verify the accuracy for the reliability of land classification. Firstly the accuracy reached 84.43%, 85.35%, and 82.68% in 1990, 2000, and 2010, respectively, based on high spatial resolution image by building a hybrid matrix. Then especially, the 2010 Globeland30 data was used as the standard to verify the forest land accuracy and the extraction accuracy reached 86.92% and 83.66% for area and location, respectively. Based on the historical land use maps, we identified that rubber plantations are the main contributor to forest loss in the region. Furthermore, in the business-as-usual scenario for the RLs, Xishuangbanna will lose 158,535 ha (158,535 × 104 m2) of forest area in next 20 years, resulting in approximately 0.23 million t (0.23 × 109 kg) CO2e emissions per year. Our framework can potentially increase the effectiveness of the REDD+ program in Xishuangbanna by accounting for a wider range of forest-controlled GHGs.

Fine Particulate Air Pollution from Electricity Generation in the US: Health Impacts by Race, Income, and Geography

Electricity generation is a large contributor to fine particulate matter (PM_2.5) air pollution. However, the demographic distribution of the resulting exposure is largely unknown. We estimate exposures to and health impacts of PM_2.5 from electricity generation in the US, for each of the seven Regional Transmission Organizations (RTOs), for each US state, by income and by race. We find that average exposures are the highest for blacks, followed by non-Latino whites. Exposures for remaining groups (e.g., Asians, Native Americans, Latinos) are somewhat lower. Disparities by race/ethnicity are observed for each income category, indicating that the racial/ethnic differences hold even after accounting for differences in income. Levels of disparity differ by state and RTO. Exposures are higher for lower-income than for higher-income, but disparities are larger by race than by income. Geographically, we observe large differences between where electricity is generated and where people experience the resulting PM_2.5 health consequences; some states are net exporters of health impacts, other are net importers. For 36 US states, most of the health impacts are attributable to emissions in other states. Most of the total impacts are attributable to coal rather than other fuels.

An Analysis of Thermal Plant Flexibility Using a National Generator Performance Database

Grid integration studies are key to understanding our ability to integrate variable generation resources into the power system and evaluating the associated costs and benefits. In these studies, it is important to understand the flexibility of the thermal power fleet, including how thermal plants operate at part load. Without a comprehensive understanding of thermal plant operation, we may over- or underestimate our ability to integrate variable generation resources and thus draw incomplete or inaccurate conclusions regarding their potential economic and environmental effects. The only public data source for understanding many elements of the operational characteristics of the thermal fleet is the U.S. Environmental Protection Agency Clean Air Markets database of historical power plant operation. However, though these data sets have been widely utilized, their use has proven to be difficult, and methods to clean and filter the data are not transparent. Here, we describe the database and a method to clean and filter it. We then use the cleaned database to demonstrate several characteristics of historical plant operation, including frequent part load operation. Finally, we provide a cleaned data set with heat rate curves and describe how to use it in general modeling activities and analysis.

How Much Are We Saving after All? Characterizing the Effects of Commonly Varying Assumptions on Emissions and Damage Estimates in PJM

In recent years, several methods have emerged to estimate the emissions and health, environmental, and climate change damages avoided by interventions such as energy efficiency, demand response, and the integration of renewables. However, differing assumptions employed in these analyses could yield contradicting recommendations regarding intervention implementation. We test the magnitude of the effect of using different key assumptions-average vs marginal emissions, year of calculation, temporal and regional scope, and inclusion of nonemitting generation-to estimate Mid-Atlantic region power pool (PJM) emissions and damage factors. We further highlight the importance of factor selection by evaluating three illustrative 2017 power system examples in PJM. We find that for a simple building lighting intervention, using average emissions factors incorporating nonemitting generation underestimates avoided damages by 45% compared to marginal factors. For PJM demand response, outdated marginal emissions factors from 2016 overestimate avoided damages by 25% compared to 2017 factors. Our assessment of PJM summer load further suggests that fossil-only average emissions factors overestimate damages by 63% compared to average factors incorporating nonemitting generation. We recommend that energy modelers carefully select appropriate emissions metrics when performing their analyses. Furthermore, since the U.S. electric grid is rapidly changing, we urge decision-makers to frequently update (and consider forecasting) grid emissions factors.