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The general conclusion of climate change studies is the necessity of eliminating net CO2 emissions in general and from the electric power systems in particular by 2050. The share of renewable energy is increasing worldwide, but due to the intermittent nature of wind and solar power, a lack of system flexibility is already hampering the further integration of renewable energy in some countries. In this study, we analyze if and how combinations of carbon pricing and power-to-gas (PtG) generation in the form of green power-to-hydrogen followed by methanation (which we refer to as PtG throughout) using captured CO2 emissions can provide transitions to deep decarbonization of energy systems. To this end, we focus on the economics of deep decarbonization of the European electricity system with the help of an energy system model. In different scenario analyses, we find that a CO2 price of 160 €/t (by 2050) is on its own not sufficient to decarbonize the electricity sector, but that a CO2 price path of 125 (by 2040) up to 160 €/t (by 2050), combined with PtG technologies, can lead to an economically feasible decarbonization of the European electricity system by 2050. These results are robust to higher than anticipated PtG costs.
Mit der Energiewende hat die Bundesregierung den Umbau der Energieversorgung begonnen. Da das Gelingen der Energiewende für die Zukunfts- und Wettbewerbsfähigkeit des Wirtschaftsstandorts Deutschland essenziell ist, wurden seitens des Bundesverbandes der deutschen Industrie (BDI) bereits 2013 Impulse für eine smarte Energiewende veröffentlicht, in denen fünf Prinzipien abgeleitet werden, die einen Rahmen für den Diskurs über die zu ergreifenden Maßnahmen setzen. Erneuerbare Energien werden in dem kommenden Jahren die dominierende Stromquelle darstellen. Daraus entstehen neue Herausforderungen. Zu deren Bewältigung hat das Bundeswirtschaftsministerium (BMWi) kürzlich eine 10-Punkte-Agenda (ZPA) für die zentralen Vorhaben der Energiewende vorgelegt. Zu diskutieren ist, inwieweit sie im Einklang mit den fünf Prinzipien des BDI steht und an welchen Stellen Anpassungen notwendig werden, damit der Umbau des Energiesystems erfolgreich gelingen kann.
The promise of the EVs is twofold. First, rejuvenating a transport sector that still heavily depends on fossil fuels and second, integrating intermittent renewable energies into the power mix. However, it is still not clear how electricity networks will cope with the predicted increase in EVs and their charging demand, especially in combination with conventional energy demand. This paper proposes a methodology which allows to predict the impact of EV charging behavior on the electricity grid. Moreover, this model simulates the driving and charging behavior of heterogeneous EV drivers which differ in their mobility pattern, decision-making heuristics and charging strategies. The simulations show that uncoordinated charging results in charging load clustering. In contrast, decentralized coordination allows to fill the valleys of the conventional load curve and to integrate EVs without the need of a costly expansion of the electricity grid.
Machine learning (ML) techniques are rapidly evolving, both in academia and practice. However, enterprises show different maturity levels in successfully implementing ML techniques. Thus, we review the state of adoption of ML in enterprises. We find that ML technologies are being increasingly adopted in enterprises, but that small and medium-size enterprises (SME) are struggling with the introduction in comparison to larger enterprises. In order to identify enablers and success factors we conduct a qualitative empirical study with 18 companies in different industries. The results show that especially SME fail to apply ML technologies due to insufficient ML knowhow. However, partners and appropriate tools can compensate this lack of resources. We discuss approaches to bridge the gap for SME.