This paper analyses some possible means by which renewable power could be integrated into the steel manufacturing process, with techniques such as blast furnace gas recirculation (BF-GR), furnaces that utilize carbon capture, a higher share of electrical arc furnaces (EAFs) and the use of direct reduced iron with hydrogen as reduction agent (H-DR). It is demonstrated that these processes could lead to less dependence on-and ultimately complete independence from-coal. This opens the possibility of providing the steel industry with power and heat by coupling to renewable power generation (sector coupling). In this context, it is shown using the example of Germany that with these technologies, reductions of 47-95% of CO 2 emissions against 1990 levels and 27-95% of primary energy demand against 2008 can be achieved through the integration of 12-274 TWh of renewable electrical power into the steel industry. Thereby, a substantial contribution to reducing CO 2 emissions and fuel demand could be made (although it would fall short of realizing the German government's target of a 50% reduction in power consumption by 2050).

With electric heat pumps substituting for fossil-fueled alternatives, the temporal variability of their power consumption becomes increasingly important to the electricity system. To easily include this variability in energy system analyses, this paper introduces the “When2Heat” dataset comprising synthetic national time series of both the heat demand and the coefficient of performance (COP) of heat pumps. It covers 16 European countries, includes the years 2008 to 2018, and features an hourly resolution. Demand profiles for space and water heating are computed by combining gas standard load profiles with spatial temperature and wind speed reanalysis data as well as population geodata. COP time series for different heat sources – air, ground, and groundwater – and different heat sinks – floor heating, radiators, and water heating – are calculated based on COP and heating curves using reanalysis temperature data. The dataset, as well as the scripts and input parameters, are publicly available under an open source license on the Open Power System Data platform.

In the scenarios reviewed, GHG emission reduction targets drive electrification. • If targets are met by 2050, 40-100% of heat and car transport will be electrified. • The electricity consumption of heat and transport will increase to 400-800 TWh.

Summary The transition to an intermittent energy production from renewable energy sources (RES) increases the complexity of providing reliable energy supply in Germany. Yet the growing number of RES impede the necessary effort to control the system. The introduction of digital or smart energy systems is often proclaimed as a logical next step towards coping with this rising complexity. It seems convenient that the manufacturing industry, as one of the major energy consumers, is currently also in a process of a digital transition, the fourth industrial revolution. This paper explores the current state of expert discourse on the role of a digitized industry as a potential enabler for the energy transition using Germany as a case study. For this purpose, we gathered qualitative data through semistructured interviews among industry managers and energy researchers. We identified the three major areas in the expert discourse of industry's future potentials: (1) increasing transparency in the energy system, (2) providing demand flexibility, and (3) increasing energy efficiency. In this paper, we address the internal barriers and explore industry's reluctance to interact with the energy system in order to initiate a transition.

The objective of this paper is to contribute to the topic of energy supply security by proposing a Monte Carlo-based and a survey-based model to analyze the costs of power interruptions. Outage cost estimations are particularly important when deciding on investments to improve supply security (e.g. additional transmission lines) in order to compare costs to benefits. But also, other policy decisions on measures that have direct or indirect consequences for the supply security (e.g. a phasing out of nuclear energy) need to be based on results from outage cost estimations. The main focus of this paper lies with residential consumers, but the model is applied to commercial, industrial and governmental consumers as well. There are limited studies that have approached the problem of evaluating outage cost. When comparing the results of these studies, they often display a high degree of diversification. As consumers have different needs and dependencies towards the supply of electricity because of varying circumstances and preferences, a great diversity in outage cost is a logical consequence. To take the high degree of uncertainties into account, a Monte Carlo simulation was conducted in this study for the case of private households in Germany.

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