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Targeting existing power plants: EPA emission reduction with wind and demand response

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  • Cardell, J.B.
  • Anderson, C.L.

Abstract

Electricity generation accounts for 40% of CO2 emissions from fossil fuel combustion in the United States. Section 111 of the Clean Air Act (CAA) allows for greenhouse gas emission regulation by the US Environmental Protection Agency (EPA). In June 2014, EPA issued the Clean Power Plan that proposes regulation of existing power plants via a “best system of emission reduction” or BSER. Reducing carbon dioxide emissions caused by electricity generation is one of the main motivations for increasing wind power and other renewable energy use, and this option is included in the BSER. This paper applies Monte Carlo simulation with a two-stage power flow optimization framework to analyze the potential CO2 emission reduction with 10% and 20% wind penetration using the proposed BSER. The results show that EPA's BSER does achieve significant emission reduction, but an increase in cost of electricity and load curtailment can result if significant wind is installed without other measures. These concerns are eliminated by including recourse to real-time demand response along with EPA's BSER, suggesting that the proposed BSER, implemented alone, could be insufficient for reaching EPA's target CO2 reductions while also safeguarding power system reliability and cost.

Suggested Citation

  • Cardell, J.B. & Anderson, C.L., 2015. "Targeting existing power plants: EPA emission reduction with wind and demand response," Energy Policy, Elsevier, vol. 80(C), pages 11-23.
    • Handle: RePEc:eee:enepol:v:80:y:2015:i:c:p:11-23
    DOI: 10.1016/j.enpol.2015.01.021
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    References listed on IDEAS

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    1. Willis, K. G. & Garrod, G. D., 1997. "Electricity supply reliability : Estimating the value of lost load," Energy Policy, Elsevier, vol. 25(1), pages 97-103, January.
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    Cited by:

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    2. Alcázar-Ortega, Manuel & Calpe, Carmen & Theisen, Thomas & Rodríguez-García, Javier, 2015. "Certification prerequisites for activities related to the trading of demand response resources," Energy, Elsevier, vol. 93(P1), pages 705-715.
    3. Bianchini, Gianni & Casini, Marco & Vicino, Antonio & Zarrilli, Donato, 2016. "Demand-response in building heating systems: A Model Predictive Control approach," Applied Energy, Elsevier, vol. 168(C), pages 159-170.
    4. Alcázar-Ortega, Manuel & Calpe, Carmen & Theisen, Thomas & Carbonell-Carretero, José Francisco, 2015. "Methodology for the identification, evaluation and prioritization of market handicaps which prevent the implementation of Demand Response: Application to European electricity markets," Energy Policy, Elsevier, vol. 86(C), pages 529-543.
    5. Min, C.G. & Park, J.K. & Hur, D. & Kim, M.K., 2016. "A risk evaluation method for ramping capability shortage in power systems," Energy, Elsevier, vol. 113(C), pages 1316-1324.
    6. Haiyan Shan & Junliang Yang & Guo Wei, 2019. "Industrial Symbiosis Systems: Promoting Carbon Emission Reduction Activities," IJERPH, MDPI, vol. 16(7), pages 1-23, March.
    7. Fuentes-Cortés, Luis Fabián & Ma, Yan & Ponce-Ortega, Jose María & Ruiz-Mercado, Gerardo & Zavala, Victor M., 2018. "Valuation of water and emissions in energy systems," Applied Energy, Elsevier, vol. 210(C), pages 518-528.

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