Short-term CO2 emissions forecasting based on decomposition approaches and its impact on electricity market scheduling. (1st January 2021)

• Record Type: Journal Article
• Authors: Bokde, Neeraj Dhanraj; Tranberg, Bo; Andresen, Gorm Bruun
• Is Part Of: Applied energy. Volume 281(2021)
• Publication Date: 2021-01-01
• Subjects: Forecasting -- CO2 emission -- Demand flexibility; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2020.116061
• Languages: English
• ISSNs: 0306-2619

Abstract

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 CO 2 emissions globally, which shows a large potential for reduction. This paper proposes a novel short-term CO 2 emissions forecast to enable intelligent scheduling of flexible electricity consumption to minimize the resulting CO 2 emissions. Two proposed time series decomposition methods are developed for short-term forecasting of the CO 2 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. Highlights: Short-term forecasting of the CO 2 emissions of electricity. Proposing two novel decomposition methods for CO 2 emissions forecasting. 48-hour horizon forecast for bidding in the day-ahead electricity market. Enables scheduling of flexible electricity consumption to minimize CO 2 emissions.