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Evaluation of a rapid LMP-based approach for calculating marginal unit emissions

Author

Listed:
  • Rogers, Michelle M.
  • Wang, Yang
  • Wang, Caisheng
  • McElmurry, Shawn P.
  • Miller, Carol J.

Abstract

To evaluate the sustainability of systems that draw power from electrical grids there is a need to rapidly and accurately quantify pollutant emissions associated with power generation. Air emissions resulting from electricity generation vary widely among power plants based on the types of fuel consumed, the efficiency of the plant, and the type of pollution control systems in service. To address this need, methods for estimating real-time air emissions from power generation based on locational marginal prices (LMPs) have been developed. Based on LMPs the type of the marginal generating unit can be identified and pollutant emissions are estimated. While conceptually demonstrated, this LMP approach has not been rigorously tested. The purpose of this paper is to (1) improve the LMP method for predicting pollutant emissions and (2) evaluate the reliability of this technique through power system simulations. Previous LMP methods were expanded to include marginal emissions estimates using an LMP Emissions Estimation Method (LEEM). The accuracy of emission estimates was further improved by incorporating a probability distribution function that characterize generator fuel costs and a membership function (MF) capable of accounting for multiple marginal generation units. Emission estimates were compared to those predicted from power flow simulations. The improved LEEM was found to predict the marginal generation type approximately 70% of the time based on typical system conditions (e.g. loads and fuel costs) without the use of a MF. With the addition of a MF, the LEEM was found to provide emission estimates with errors typically less than 25% for CO2, and less than 50% for SO2 and NOX. Overall, the LEEM presented provides a means of incorporating pollutant emissions into demand side decisions.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:111:y:2013:i:c:p:812-820
    DOI: 10.1016/j.apenergy.2013.05.057
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    Citations

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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. Norouzi, Hadi & Abedi, Sajjad & Jamalzadeh, Reza & Rad, Milad Ghiasi & Hosseinian, Seyed Hossein, 2014. "Modeling and investigation of harmonic losses in optimal power flow and power system locational marginal pricing," Energy, Elsevier, vol. 68(C), pages 140-147.
    3. 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.
    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. Biéron, M. & Le Dréau, J. & Haas, B., 2023. "Assessment of the marginal technologies reacting to demand response events: A French case-study," Energy, Elsevier, vol. 275(C).
    6. 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.
    7. Hawkes, A.D., 2014. "Long-run marginal CO2 emissions factors in national electricity systems," Applied Energy, Elsevier, vol. 125(C), pages 197-205.
    8. 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.
    9. Archsmith, James & Kendall, Alissa & Rapson, David, 2015. "From Cradle to Junkyard: Assessing the Life Cycle Greenhouse Gas Benefits of Electric Vehicles," Research in Transportation Economics, Elsevier, vol. 52(C), pages 72-90.
    10. 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.
    11. Heydarzadeh, Zahra & Mac Kinnon, Michael & Thai, Clinton & Reed, Jeff & Brouwer, Jack, 2020. "Marginal methane emission estimation from the natural gas system," Applied Energy, Elsevier, vol. 277(C).

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