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Bo Tranberg
Researcher at Aarhus University
Publications - 24
Citations - 421
Bo Tranberg is an academic researcher from Aarhus University. The author has contributed to research in topics: Electricity & Renewable energy. The author has an hindex of 9, co-authored 24 publications receiving 254 citations.
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Journal ArticleDOI
Real-time carbon accounting method for the European electricity markets
TL;DR: In this article, a real-time consumption-based accounting approach based on flow tracing is proposed to trace power flows from producer to consumer, in contrast to the traditional input-output models of carbon accounting.
Journal ArticleDOI
Optimal heterogeneity in a simplified highly renewable European electricity system
TL;DR: In this article, spatial distributions of renewable assets are explored which exploit this heterogeneity to lower the total system costs for a high level of renewable electricity in Europe using several intuitive heuristic algorithms, optimal portfolio theory and a local search algorithm are used to find optimal distribution of renewable generation capacities that minimise the total costs of backup, transmission and renewable capacity simultaneously.
Journal ArticleDOI
Real-Time Carbon Accounting Method for the European Electricity Markets.
TL;DR: In this article, a real-time consumption-based accounting approach based on flow tracing is proposed to trace power flows from producer to consumer, in contrast to the traditional input-output models of carbon accounting.
Journal ArticleDOI
Short-term CO2 emissions forecasting based on decomposition approaches and its impact on electricity market scheduling
TL;DR: In this article, two time series decomposition methods are developed for short-term forecasting of the CO 2 emissions of electricity, which are in turn benchmarked against a set of state-of-the-art models.
Journal ArticleDOI
Short-term CO2 emissions forecasting based on decomposition approaches and its impact on electricity market scheduling.
TL;DR: In this article, two time series decomposition methods are developed for short-term forecasting of the CO2 emissions of electricity, which are in turn benchmarked against a set of state-of-the-art models.