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Short-term CO2 emissions forecasting based on decomposition approaches and its impact on electricity market scheduling

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  • Bokde, Neeraj Dhanraj
  • Tranberg, Bo
  • Andresen, Gorm Bruun

Abstract

The world is facing major challenges related to global warming and emissions of greenhouse gases is a major causing factor. In 2017, energy industries accounted for 46% of all CO2 emissions globally, which shows a large potential for reduction. This paper proposes a novel short-term CO2 emissions forecast to enable intelligent scheduling of flexible electricity consumption to minimize the resulting CO2 emissions. Two proposed time series decomposition methods are developed for short-term forecasting of the CO2 emissions of electricity. These are in turn bench-marked against a set of state-of-the-art models. The result is a new forecasting method with a 48-hour horizon targeted the day-ahead electricity market. Forecasting benchmarks for France show that the new method has a mean absolute percentage error that is 25% lower than the best performing state-of-the-art model. Further, application of the forecast for scheduling flexible electricity consumption is studied for five European countries. Scheduling a flexible block of 4 h of electricity consumption in a 24 h interval can on average reduce the resulting CO2 emissions by 25% in France, 17% in Germany, 69% in Norway, 20% in Denmark, and just 3% in Poland when compared to consuming at random intervals during the day.

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  • 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).
    • Handle: RePEc:eee:appene:v:281:y:2021:i:c:s0306261920314926
    DOI: 10.1016/j.apenergy.2020.116061
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    2. Zbigniew Skibko & Magdalena Tymińska & Wacław Romaniuk & Andrzej Borusiewicz, 2021. "Impact of the Wind Turbine on the Parameters of the Electricity Supply to an Agricultural Farm," Sustainability, MDPI, vol. 13(13), pages 1-15, June.
    3. Konstantinos Bletsas & Georgios Oikonomou & Minas Panagiotidis & Eleftherios Spyromitros, 2022. "Carbon Dioxide and Greenhouse Gas Emissions: The Role of Monetary Policy, Fiscal Policy, and Institutional Quality," Energies, MDPI, vol. 15(13), pages 1-24, June.
    4. Bowen Zhang & Hongda Tian & Adam Berry & Hao Huang & A. Craig Roussac, 2024. "Experimental Comparison of Two Main Paradigms for Day-Ahead Average Carbon Intensity Forecasting in Power Grids: A Case Study in Australia," Sustainability, MDPI, vol. 16(19), pages 1-20, October.
    5. Can Ding & Yiyuan Zhou & Qingchang Ding & Kaiming Li, 2022. "Integrated Carbon-Capture-Based Low-Carbon Economic Dispatch of Power Systems Based on EEMD-LSTM-SVR Wind Power Forecasting," Energies, MDPI, vol. 15(5), pages 1-27, February.
    6. Hamels, Sam & Himpe, Eline & Laverge, Jelle & Delghust, Marc & Van den Brande, Kjartan & Janssens, Arnold & Albrecht, Johan, 2021. "The use of primary energy factors and CO2 intensities for electricity in the European context - A systematic methodological review and critical evaluation of the contemporary literature," Renewable and Sustainable Energy Reviews, Elsevier, vol. 146(C).
    7. Negri, Simone & Giani, Federico & Blasuttigh, Nicola & Massi Pavan, Alessandro & Mellit, Adel & Tironi, Enrico, 2022. "Combined model predictive control and ANN-based forecasters for jointly acting renewable self-consumers: An environmental and economical evaluation," Renewable Energy, Elsevier, vol. 198(C), pages 440-454.
    8. Yuan, Hong & Ma, Xin & Ma, Minda & Ma, Juan, 2024. "Hybrid framework combining grey system model with Gaussian process and STL for CO2 emissions forecasting in developed countries," Applied Energy, Elsevier, vol. 360(C).
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    11. Song, Chao & Wang, Tao & Chen, Xiaohong & Shao, Quanxi & Zhang, Xianqi, 2023. "Ensemble framework for daily carbon dioxide emissions forecasting based on the signal decomposition–reconstruction model," Applied Energy, Elsevier, vol. 345(C).

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    Keywords

    Forecasting; CO2 emission; Demand flexibility;
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