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ID:
124504
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| Publication |
2013.
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| Summary/Abstract |
The European electricity market is linked to a carbon market with a fixed cap that limits greenhouse gas emissions. At the same time, a number of energy efficiency policy instruments in the EU aim at reducing the electricity consumption. This article explores the interactions between the EU's carbon market on the one hand and instruments specifically targeted towards energy end-use efficiency on the other hand. Our theoretical analysis shows how electricity demand reduction triggered by energy efficiency policy instruments affects the emission trading scheme. Without adjustments of the fixed cap, decreasing electricity demand (relative to business-as-usual) reduces the carbon price without reducing total emissions. With lower carbon prices, costly low emission processes will be substituted by cheaper high emitting processes. Possible electricity and carbon price effects of electricity demand reduction scenarios under various carbon caps are quantified with a long-term electricity market simulation model. The results show that electricity efficiency policies allow for a significant reduction of the carbon cap. Compared to the 2005 emission level, 30% emission reductions can be achieved by 2020 within the emission trading scheme with similar or even lower costs for the industrial sector than were expected when the cap was initially set for a 21% emission reduction.
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| 2 |
ID:
166943
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| Summary/Abstract |
The European electricity market is immersed in an integration process that requires a fundamental transformation. In this process, Flow-Based Market Coupling, which was employed for the first time in the Central Western Europe electricity market in 2015 as a means to manage cross-border capacity allocation, is a crucial cornerstone. The novelty of this paper lies in the analysis of the price convergence or congestion across the Central Western Europe region since the Flow-Based Market Coupling was implemented. We propose using random forests to build learning models that are trained and tested with features from connected markets of this region during 2016 and 2017. These machine learning models are used for mining knowledge about our target variable, price equalization. To search for robust predictive patterns that decision-makers can use to understand congestion situations, we have tested different combinations of learning schemes, several estimators and different model parameters. The results of all implemented models are robust and reveal that promoting renewable energy can contradict the integration of the electricity market if the grid network and, in particular, the transmission lines are not adapted to the new paradigm.
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