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Residential emissions reductions through variable timing of electricity consumption

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  • Harris, A.R.
  • Rogers, Michelle Marinich
  • Miller, Carol J.
  • McElmurry, Shawn P.
  • Wang, Caisheng

Abstract

A real-time electricity emissions estimating tool, the Locational Marginal Price Emissions Estimation Method (LEEM), is assessed for its ability to reduce emissions of sulfur dioxide (SO2), nitrogen oxides (NOx), global warming potential measured as carbon dioxide equivalent (CO2e), mercury (Hg), and lead (Pb) on a residential scale. Through LEEM, residential electricity use can be shifted to low emissions times of day. In the study area of Michigan, USA emissions from five types of appliances (hot water heater, refrigerator defrost, dishwasher, clothes washer, and clothes dryer) were calculated to be theoretically reduced by 21–35% annually through a “best-case” application of LEEM. Annual emissions of the five pollutants, SO2, NOx, CO2e, Hg, and Pb, can be reduced across the state by 429,000, 110,000, 87,240,000, 2.21, and 4.53 pounds, respectively – all without a reduction in the electricity used in the period of study. Despite different fuel mixes, similar emissions reductions were calculated for other regions of the country, as well.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:158:y:2015:i:c:p:484-489
    DOI: 10.1016/j.apenergy.2015.08.042
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    1. Li, Lihui & Wen, Tao, 2013. "Estimation of C-MGARCH models based on the MBP method," Statistics & Probability Letters, Elsevier, vol. 83(2), pages 665-673.
    2. Amor, Mourad Ben & Pineau, Pierre-Olivier & Gaudreault, Caroline & Samson, Réjean, 2011. "Electricity trade and GHG emissions: Assessment of Quebec's hydropower in the Northeastern American market (2006-2008)," Energy Policy, Elsevier, vol. 39(3), pages 1711-1721, March.
    3. Le, Vo Phuong Mai & Meenagh, David, 2013. "Testing and Estimating Models Using Indirect Inference," Cardiff Economics Working Papers E2013/8, Cardiff University, Cardiff Business School, Economics Section.
    4. Bouezmarni Taoufik & Ghouch El & Taamouti Abderrahim, 2013. "Bernstein estimator for unbounded copula densities," Statistics & Risk Modeling, De Gruyter, vol. 30(4), pages 343-360, December.
    5. Phillips, Peter C.B. & Li, Degui & Gao, Jiti, 2017. "Estimating smooth structural change in cointegration models," Journal of Econometrics, Elsevier, vol. 196(1), pages 180-195.
    6. 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.
    7. Vahidinasab, V. & Jadid, S., 2009. "Multiobjective environmental/techno-economic approach for strategic bidding in energy markets," Applied Energy, Elsevier, vol. 86(4), pages 496-504, April.
    8. Valenzuela, Jorge & Thimmapuram, Prakash R. & Kim, Jinho, 2012. "Modeling and simulation of consumer response to dynamic pricing with enabled technologies," Applied Energy, Elsevier, vol. 96(C), pages 122-132.
    9. 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.
    10. Faruqui, Ahmad & Sergici, Sanem & Sharif, Ahmed, 2010. "The impact of informational feedback on energy consumption—A survey of the experimental evidence," Energy, Elsevier, vol. 35(4), pages 1598-1608.
    11. Hawkes, A.D., 2014. "Long-run marginal CO2 emissions factors in national electricity systems," Applied Energy, Elsevier, vol. 125(C), pages 197-205.
    12. Ross Kendall & Tim Ng, 2013. "Estimated Taylor Rules updated for the post-crisis period," Reserve Bank of New Zealand Analytical Notes series AN2013/04, Reserve Bank of New Zealand.
    13. Matyas Barczy & Leif Doering & Zenghu Li & Gyula Pap, 2012. "On parameter estimation for critical affine processes," Papers 1210.1866, arXiv.org, revised Mar 2013.
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