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Temporal CO2 emissions associated with electricity generation: Case study of Singapore

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  • Finenko, Anton
  • Cheah, Lynette

Abstract

Studying temporal patterns in emissions associated with electricity generation is increasingly important. On the supply side, there is interest in integrating renewable energy sources (solar, wind), which are known to vary daily and hourly. On the demand side, the concept of demand response is driving a need to better understand the impact of peak versus off-peak loading, with the objective of maximizing efficiency. In this study, we examine the case of electric power generation in Singapore, and aim to assess the half-hourly variation in associated average carbon dioxide emissions. Given the country’s serious push for clean energy solutions and a possibility of adopting carbon trading in the future, we feel the need to address the currently existing gap in research on daily CO2 emissions patterns. By associating representative electricity generation data with the characterized fleet of power plants, half-hourly emissions are found to range between 415 and 455kg CO2 per MWh. Marginal emission factors show a fluctuating daily pattern between 390 and 800kg CO2/MWh. Policy makers able to work with real generation data can use this approach to understand the carbon footprint of short-term supply and demand interventions.

Suggested Citation

  • Finenko, Anton & Cheah, Lynette, 2016. "Temporal CO2 emissions associated with electricity generation: Case study of Singapore," Energy Policy, Elsevier, vol. 93(C), pages 70-79.
    • Handle: RePEc:eee:enepol:v:93:y:2016:i:c:p:70-79
    DOI: 10.1016/j.enpol.2016.02.039
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    Citations

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    Cited by:

    1. Bokde, Neeraj Dhanraj & Tranberg, Bo & Andresen, Gorm Bruun, 2021. "Short-term CO2 emissions forecasting based on decomposition approaches and its impact on electricity market scheduling," Applied Energy, Elsevier, vol. 281(C).
    2. Rupp, Matthias & Handschuh, Nils & Rieke, Christian & Kuperjans, Isabel, 2019. "Contribution of country-specific electricity mix and charging time to environmental impact of battery electric vehicles: A case study of electric buses in Germany," Applied Energy, Elsevier, vol. 237(C), pages 618-634.
    3. L. (Lisa B.) Ryan & Sarah La Monaca & Linda Mastrandrea & Petr Spodniak, 2018. "Harnessing electricity retail tariffs to support climate change policy," Open Access publications 10197/9911, School of Economics, University College Dublin.
    4. 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.
    5. Tharsis Teoh & Oliver Kunze & Chee-Chong Teo & Yiik Diew Wong, 2018. "Decarbonisation of Urban Freight Transport Using Electric Vehicles and Opportunity Charging," Sustainability, MDPI, vol. 10(9), pages 1-20, September.
    6. You, Siming & Neoh, Koon Gee & Tong, Yen Wah & Dai, Yanjun & Wang, Chi-Hwa, 2017. "Variation of household electricity consumption and potential impact of outdoor PM2.5 concentration: A comparison between Singapore and Shanghai," Applied Energy, Elsevier, vol. 188(C), pages 475-484.
    7. Baumgärtner, Nils & Delorme, Roman & Hennen, Maike & Bardow, André, 2019. "Design of low-carbon utility systems: Exploiting time-dependent grid emissions for climate-friendly demand-side management," Applied Energy, Elsevier, vol. 247(C), pages 755-765.

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