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.
Many decision contexts are characterized by deep uncertainty where there is disagreement about values and probabilities such as policy and technological uncertainties for energy sector investments. Although there are methods for decision analysis in these contexts, there are few simple metrics to guide analysts and decision-makers on whether more sophisticated methods are appropriate, to highlight aspects of robust decision-making, and to prioritize information gathering on uncertainties. Here, we introduce a screening metric called “capacity at risk” and two complementary metrics—robust capacity and risk ratio—for identifying the most decision-relevant uncertainties and for understanding which investments could be robust and which are more uncertain across a range of different futures. The use of deterministic model runs in calculating capacity at risk metrics can lower barriers to entry for modelers and communications with stakeholders. These metrics are applied to an illustrative example of electric sector decarbonization in the United States using a detailed capacity planning and dispatch model. Scenario results demonstrate the importance of climate policy targets and timing on decisions, while uncertainties such as natural gas prices and renewable costs have more moderate impacts on planning. We also apply the capacity at risk framework to other prominent U.S. electric sector scenario analysis. These comparisons suggest that commonly used scenarios may understate uncertainty, giving decision-makers a misleading sense of portfolio risk and understating the value of frameworks that explicitly assess decisions under uncertainty.
Studies of power system operation commonly draw from two key databases produced by the US Environmental Protection Agency: the Acid Rain Program’s Continuous Emission Monitoring System (CEMS) data, and the Emissions and Generation Resource Integrated Database (eGRID). Separate reporting requirements and heterogeneity in data aggregation between these two databases creates a barrier to systematic spatial and temporal retrospective power system analysis. This work describes the inherent challenges to this undertaking and documents a method for reconciling the two seemingly disparate data sources. While fundamental differences in data reporting and aggregation prevent us from achieving full coverage, this work represents an important initial step to aligning these two repositories of US power system data. We demonstrate the value of this linkage by computing relative unit-level, hourly utilization metrics for most thermal power plants in the US. Analysis of these metrics across time illustrates thermal generator cycling trends in California between 2011 and 2017. These unit-level results indicate that combined cycle units within California increased their part-load generation by 15% and resultant CO2 emissions by 17% and decreased their start/stop frequency over time by 8.5% and resultant emissions by 47%. Open cycle gas turbines overall increased their generation- number of start/stop cycles by 97% and resultant emissions by 85%, part-load generation by 120% and resultant emissions by 100%, and full load generation by 40% and resultant emissions 18%. We also observe a temporal shift in thermal generation from morning hours to evening in California.
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