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Residential demand response reduces air pollutant emissions on peak electricity demand days in New York City

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  • Gilbraith, Nathaniel
  • Powers, Susan E.

Abstract

Many urban areas in the United States have experienced difficulty meeting the National Ambient Air Quality Standards (NAAQS), partially due to pollution from electricity generating units. We evaluated the potential for residential demand response to reduce pollutant emissions on days with above average pollutant emissions and a high potential for poor air quality. The study focused on New York City (NYC) due to non-attainment with NAAQS standards, large exposed populations, and the existing goal of reducing pollutant emissions. The baseline demand response scenario simulated a 1.8% average reduction in NYC peak demand on 49 days throughout the summer. Nitrogen oxide and particulate matter less than 2.5μm in diameter emission reductions were predicted to occur (−70, −1.1metric tons (MT) annually), although, these were not likely to be sufficient for NYC to meet the NAAQS. Air pollution mediated damages were predicted to decrease by $100,000–$300,000 annually. A sensitivity analysis predicted that substantially larger pollutant emission reductions would occur if electricity demand was shifted from daytime hours to nighttime hours, or the total consumption decreased. Policies which incentivize shifting electricity consumption away from periods of high human and environmental impacts should be implemented, including policies directed toward residential consumers.

Suggested Citation

  • Gilbraith, Nathaniel & Powers, Susan E., 2013. "Residential demand response reduces air pollutant emissions on peak electricity demand days in New York City," Energy Policy, Elsevier, vol. 59(C), pages 459-469.
  • Handle: RePEc:eee:enepol:v:59:y:2013:i:c:p:459-469
    DOI: 10.1016/j.enpol.2013.03.056
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    References listed on IDEAS

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    1. Newsham, Guy R. & Bowker, Brent G., 2010. "The effect of utility time-varying pricing and load control strategies on residential summer peak electricity use: A review," Energy Policy, Elsevier, vol. 38(7), pages 3289-3296, July.
    2. Muller, Nicholas Z. & Mendelsohn, Robert, 2007. "Measuring the damages of air pollution in the United States," Journal of Environmental Economics and Management, Elsevier, vol. 54(1), pages 1-14, July.
    3. Allcott, Hunt, 2011. "Rethinking real-time electricity pricing," Resource and Energy Economics, Elsevier, vol. 33(4), pages 820-842.
    4. Ashok, S. & Banerjee, R., 2003. "Optimal cool storage capacity for load management," Energy, Elsevier, vol. 28(2), pages 115-126.
    5. Gottwalt, Sebastian & Ketter, Wolfgang & Block, Carsten & Collins, John & Weinhardt, Christof, 2011. "Demand side management—A simulation of household behavior under variable prices," Energy Policy, Elsevier, vol. 39(12), pages 8163-8174.
    6. Ahmad Faruqui & Sanem Sergici, 2010. "Household response to dynamic pricing of electricity: a survey of 15 experiments," Journal of Regulatory Economics, Springer, vol. 38(2), pages 193-225, October.
    7. Nicholas Z. Muller & Robert Mendelsohn, 2009. "Efficient Pollution Regulation: Getting the Prices Right," American Economic Review, American Economic Association, vol. 99(5), pages 1714-1739, December.
    8. Hawkes, A.D., 2010. "Estimating marginal CO2 emissions rates for national electricity systems," Energy Policy, Elsevier, vol. 38(10), pages 5977-5987, October.
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    2. Nsanzineza, Rene & O’Connell, Matthew & Brinkman, Gregory & Milford, Jana B., 2017. "Emissions implications of downscaled electricity generation scenarios for the western United States," Energy Policy, Elsevier, vol. 109(C), pages 601-608.
    3. Smith, Alexander M. & Brown, Marilyn A., 2015. "Demand response: A carbon-neutral resource?," Energy, Elsevier, vol. 85(C), pages 10-22.
    4. Krieger, Elena M. & Casey, Joan A. & Shonkoff, Seth B.C., 2016. "A framework for siting and dispatch of emerging energy resources to realize environmental and health benefits: Case study on peaker power plant displacement," Energy Policy, Elsevier, vol. 96(C), pages 302-313.
    5. Guo, Peiyang & Li, Victor O.K. & Lam, Jacqueline C.K., 2017. "Smart demand response in China: Challenges and drivers," Energy Policy, Elsevier, vol. 107(C), pages 1-10.
    6. Howard, B. & Waite, M. & Modi, V., 2017. "Current and near-term GHG emissions factors from electricity production for New York State and New York City," Applied Energy, Elsevier, vol. 187(C), pages 255-271.
    7. Nikolaos Iliopoulos & Motoharu Onuki & Miguel Esteban, 2021. "Shedding Light on the Factors That Influence Residential Demand Response in Japan," Energies, MDPI, vol. 14(10), pages 1-23, May.
    8. Bastida, Leire & Cohen, Jed J. & Kollmann, Andrea & Moya, Ana & Reichl, Johannes, 2019. "Exploring the role of ICT on household behavioural energy efficiency to mitigate global warming," Renewable and Sustainable Energy Reviews, Elsevier, vol. 103(C), pages 455-462.

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