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Locational marginal emissions: Analysis of pollutant emission reduction through spatial management of load distribution

Author

Listed:
  • Wang, Y.
  • Wang, C.
  • Miller, C.J.
  • McElmurry, S.P.
  • Miller, S.S.
  • Rogers, M.M.

Abstract

Environmental concerns associated with power generation drive an increasing interest in developing load management strategies to reduce pollutant emissions. Currently, no mechanism exists to directly influence pollutant emissions based on demand-side decisions. This shortcoming is addressed through the exploration of an alternative load distribution management paradigm based on the use of locational marginal emissions (LMEs). LMEs present a novel mechanism for optimizing load based on pollutant emissions. To demonstrate the application of LMEs, simulation studies using the IEEE 14-bus system and a large regional transmission system in the US (PJM) were performed and changes in CO2, SO2, and NOx emissions were quantified for varying levels of spatial load flexibility. The simulation results confirm that the proposed LME-based load management method is effective in reducing pollutant emissions in comparison to the traditional economic load distribution management method based on the locational marginal price (LMP). Emission reductions were found to become more significant as the proportion of spatially controllable loads increased. Adoption of LMEs by independent system operators (ISOs) or Regional Transmission Organizations (RTOs) would empower demand-side clients to reduce pollutant emissions based on their own load management decisions and enhance the sustainability of free-market power systems. Alternately, the LME management scheme could be automated by utilities through connections to Smart Grid compatible appliances.

Suggested Citation

  • Wang, Y. & Wang, C. & Miller, C.J. & McElmurry, S.P. & Miller, S.S. & Rogers, M.M., 2014. "Locational marginal emissions: Analysis of pollutant emission reduction through spatial management of load distribution," Applied Energy, Elsevier, vol. 119(C), pages 141-150.
    • Handle: RePEc:eee:appene:v:119:y:2014:i:c:p:141-150
    DOI: 10.1016/j.apenergy.2013.12.052
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    References listed on IDEAS

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    Cited by:

    1. Hu, Tingli & Wang, Caisheng & Miller, Carol, 2021. "Identification of marginal generation units based on publicly available information," Applied Energy, Elsevier, vol. 281(C).
    2. Park, Byungkwon & Dong, Jin & Liu, Boming & Kuruganti, Teja, 2023. "Decarbonizing the grid: Utilizing demand-side flexibility for carbon emission reduction through locational marginal emissions in distribution networks," Applied Energy, Elsevier, vol. 330(PA).
    3. Lara J. Treemore-Spears & J. Morgan Grove & Craig K. Harris & Lawrence D. Lemke & Carol J. Miller & Kami Pothukuchi & Yifan Zhang & Yongli L. Zhang, 2016. "A workshop on transitioning cities at the food-energy-water nexus," Journal of Environmental Studies and Sciences, Springer;Association of Environmental Studies and Sciences, vol. 6(1), pages 90-103, March.
    4. Bigazzi, Alexander, 2019. "Comparison of marginal and average emission factors for passenger transportation modes," Applied Energy, Elsevier, vol. 242(C), pages 1460-1466.
    5. Harris, A.R. & Rogers, Michelle Marinich & Miller, Carol J. & McElmurry, Shawn P. & Wang, Caisheng, 2015. "Residential emissions reductions through variable timing of electricity consumption," Applied Energy, Elsevier, vol. 158(C), pages 484-489.
    6. Chaparro, Iván & Watts, David & Gil, Esteban, 2017. "Modeling marginal CO2 emissions in hydrothermal systems: Efficient carbon signals for renewables," Applied Energy, Elsevier, vol. 204(C), pages 318-331.
    7. Amir Shahin Kamjou & Carol J. Miller & Mahdi Rouholamini & Caisheng Wang, 2021. "Comparison between Historical and Real-Time Techniques for Estimating Marginal Emissions Attributed to Electricity Generation," Energies, MDPI, vol. 14(17), pages 1-15, August.

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