From Regions to Stacks: Spatial and Temporal Downscaling of Power Pollution Scenarios

Hobbs, Benjamin F.; Hu, Ming-Che; Chen, Yihsu; Ellis, J.; Paul, Anthony; Burtraw, Dallas; Palmer, Karen

doi:10.1109/tpwrs.2009.2036801

Cited by 17 publications

(6 citation statements)

References 19 publications

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“…Alternatively, approaches such as the Emission Scenario Projection method translate regional growth factors into county-level, pollutant-specific and source category-specific factors (Loughlin et al, 2011;Ran et al, 2015) that can be used in detailed air quality modeling exercises. In estimating impacts, it may also be advantageous to integrate models and methods that site new power sector and industrial facilities based upon heuristics, consistent with other scenario assumptions (e.g., Hobbs et al, 2010;Kraucunas et al, 2015).…”

Section: Future Directionsmentioning

confidence: 99%

Role of future scenarios in understanding deep uncertainty in long-term air quality management

Gamas

Dodder

Loughlin

et al. 2015

Journal of the Air & Waste Management Association

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The environment and its interactions with human systems, whether economic, social, or political, are complex. Relevant drivers may disrupt system dynamics in unforeseen ways, making it difficult to predict future conditions. This kind of "deep uncertainty" presents a challenge to organizations faced with making decisions about the future, including those involved in air quality management. Scenario Planning is a structured process that involves the development of narratives describing alternative future states of the world, designed to differ with respect to the most critical and uncertain drivers. The resulting scenarios are then used to understand the consequences of those futures and to prepare for them with robust management strategies. We demonstrate a novel air quality management application of Scenario Planning. Through a series of workshops, important air quality drivers were identified. The most critical and uncertain drivers were found to be "technological development" and "change in societal paradigms." These drivers were used as a basis to develop four distinct scenario storylines. The energy and emissions implications of each storyline were then modeled using the MARKAL energy system model. NO x emissions were found to decrease for all scenarios, largely a response to existing air quality regulations, whereas SO 2 emissions ranged from 12% greater to 7% lower than 2015 emissions levels. Future-year emissions differed considerably from one scenario to another, however, with key differentiating factors being transition to cleaner fuels and energy demand reductions.Implications: Application of scenarios in air quality management provides a structured means of sifting through and understanding the dynamics of the many complex driving forces affecting future air quality. Further, scenarios provide a means to identify opportunities and challenges for future air quality management, as well as a platform for testing the efficacy and robustness of particular management options across wide-ranging conditions. IntroductionIn the United States, air quality managers at the federal and state levels develop emission reduction strategies to meet air quality standards. Emission inventories are projected into the future, both with and without each candidate strategy. Air quality models are then used to evaluate the resulting changes in air quality. At the federal level, illustrative emission reduction strategies are a key component of Regulatory Impact Analyses (RIAs) (e.g., U.S. Environmental Protection Agency [EPA], 2009[EPA], , 2012a[EPA], 2012b[EPA], , 2014). These strategies demonstrate how an air quality standard can be met and are used in estimating the resulting costs and health and environmental benefits. For states, emission reduction strategies often support State Implementation Plans (SIPs), which specify how the state will comply with air quality standards into the future (e.g., California Air Resources Board [CARB], 2007; Texas Commission on Environmental Quality [TCEQ], 2012). In RIA and SIP applicatio...

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Section: Future Directionsmentioning

confidence: 99%

Role of future scenarios in understanding deep uncertainty in long-term air quality management

Gamas

Dodder

Loughlin

et al. 2015

Journal of the Air & Waste Management Association

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“…Objective Function min f(x) = sum(a.xPc.xPc +b.xPc +c) (9) where PG is the active power output of controllable generators, a, b, c are the fuel cost coefficient vectors, .x is element-wise multiplication between vectors or between matrices. …”

Section: Generation Dispatch Model With Air Pollutant Dispersionmentioning

confidence: 99%

“…Even for the same amount of pollutant, due to the geographical distribution and meteorological condition, the effect for a city might be completely different. This intuitive idea has been well identified in environmental science [9]- [11]. Many efforts have been made to establish sophisticated models for pollutant dispersion with different meteorological conditions [12].…”

Section: Introductionmentioning

confidence: 99%

Generation dispatch with air pollutant dispersion consideration

Hou

Wang

et al. 2013

2013 IEEE Power &Amp; Energy Society General Meeting

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This paper addresses a new generation dispatch method with constraints of geographical emission distribution of air pollutant dispersion. Traditionally, generation dispatch methodology considers the total cost and total amount of emission. However, even for the same amount of emission, the effect of emitted S02, NOx and particles might be completed different due to the geographical distribution and meteorological condition. In this work, pollutants distribution associated with geographical distribution of power plants and meteorological condition will be integrated into the generation dispatch models. The generation dispatch pattern is thus established with acceptable air pollution level for a specified location. A highly efficient interior point method is employed to solve the proposed model. A standard IEEE test system is employed to demonstrate the proposed model and algorithms. Index Terms--Air pollution dispersion model, generation dispatch, interior point method I.

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“…Life cycle assessment (LCA) provides a robust method for examining these upstream and downstream emissions as a cradle-to-grave approach to quantifying the environmental burdens of products or processes from materials extraction to waste disposal (cradle to grave). , Present emissions models, however, are limited in their capability to estimate life cycle emissions changes at subnational scales and hourly time steps. , When quantifying the life cycle emissions of an electricity grid, national assumptions about the generation mixes are typically applied, neglecting to account for the regionalized differences and temporal dynamics implicit to power systems that can result in variable emissions results . Similar challenges have been noted for other air pollutants , and water consumption. − Data that characterize dynamic grid interactions can result in more realistic life cycle emissions and nuanced understanding of their spatial and temporal distributions, but that requires that LCAs leverage information at more refined spatiotemporal resolutions. − …”

Section: Introductionmentioning

confidence: 99%

Grid-Scale Life Cycle Greenhouse Gas Implications of Renewable, Storage, and Carbon Pricing Options

Jordaan

Hobbs

2020

Environ. Sci. Technol.

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Models that characterize life cycle greenhouse gases from electricity generation are limited in their capability to estimate emissions changes at scales that capture the grid-scale benefits of technologies and policies that enhance renewable systems integration. National assumptions about generation mixes are often applied at annual time steps, neglecting spatiotemporal resolutions that provide insights on impacts from time-variable emissions. Our grid-scale model incorporates details of transmission and generation planning that allows a geographically and temporally textured and more realistic assessment of the life cycle greenhouse gas emissions outcomes, using a case study of the Western Interconnection of North America. Results from a co-optimized model of generation, transmission, and operationsthe Johns Hopkins Stochastic Multistage Integrated Network Expansion Modelprovide a detailed characterization of twenty-one scenarios with different configurations of storage additions, new renewable capacity, and carbon prices. Life cycle results suggest that optimization models that focus on generation alone may underestimate emissions by 18−29% because only emissions from power generation are quantified (i.e., supply chain emissions are omitted) but also that carbon pricing is the predominant driver of reducing emissions in the scenarios we examine. Life cycle assessment of electricity generation should move beyond individual technologies toward capturing the influence of policies at the system level to better understand technology-policy dynamics for the grid.

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From Regions to Stacks: Spatial and Temporal Downscaling of Power Pollution Scenarios

Cited by 17 publications

References 19 publications

Role of future scenarios in understanding deep uncertainty in long-term air quality management

Role of future scenarios in understanding deep uncertainty in long-term air quality management

Generation dispatch with air pollutant dispersion consideration

Grid-Scale Life Cycle Greenhouse Gas Implications of Renewable, Storage, and Carbon Pricing Options

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