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


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


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

    1. Liao, Gwo-Ching, 2011. "A novel evolutionary algorithm for dynamic economic dispatch with energy saving and emission reduction in power system integrated wind power," Energy, Elsevier, vol. 36(2), pages 1018-1029.
    2. Rogers, Michelle M. & Wang, Yang & Wang, Caisheng & McElmurry, Shawn P. & Miller, Carol J., 2013. "Evaluation of a rapid LMP-based approach for calculating marginal unit emissions," Applied Energy, Elsevier, vol. 111(C), pages 812-820.
    3. Stephen P. Holland & Erin T. Mansur, 2008. "Is Real-Time Pricing Green? The Environmental Impacts of Electricity Demand Variance," The Review of Economics and Statistics, MIT Press, vol. 90(3), pages 550-561, August.
    4. ., 2013. "Application of auction theory in China," Chapters,in: Cartels, Competition and Public Procurement, chapter 8, pages 128-136 Edward Elgar Publishing.
    5. ., 2013. "Auction theory and collusion," Chapters,in: Cartels, Competition and Public Procurement, chapter 4, pages 36-62 Edward Elgar Publishing.
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    Cited by:

    1. 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.
    2. 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.
    3. repec:eee:appene:v:204:y:2017:i:c:p:318-331 is not listed on IDEAS


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