The Energy Turnaround

Abstract

When the tsunami hit the Fukushima atomic plant in Japan in March 2011, causing the reactor disaster, it was impossible to predict the consequences for the future of energy supply. Many countries have considerably strengthened their activities since then to realize an energy turnaround, i.e., a switch from nuclear power and fossil fuels to a more sustainable energy supply with increased use of regenerative energy resources such as wind or sun. Whereas only 55 states had political ambitions to expand the use of renewable energies in 2005, this number has increased to about 190 states in 2012. Also a change in attitudes can be observed. Early in 2012, more than 70 % of the French supported a phase-out of nuclear power -even though France was worldwide frontrunner in this area with a 75 % nuclear powered energy supply in the end of 2010. Germany takes an uncompromising approach in that it has officially decided to phase out nuclear power completely by 2022, although the economically strong parts in the country's south receive more than 50 % of their electricity from nuclear power plants. This development towards a nuclear phase-out, which can be observed in other countries to a lesser extent, can be considered -in the jargon of BISE -as a 'revolution' more than an 'evolution' . A revolution always comes with risks, but also offers opportunities. In the following we will show that the energy turnaround opens up a wide range of opportunities -if it is addressed responsibly and with a well-conceived plan.First of all it should be mentioned that this 'revolution' may be a current topic, but in principle is not a new one. Business & Information Systems Engineering (BISE) addressed this issue in a very early stage. For example, in issue 4/2008, the high potential of IS beyond Green IT regarding an energy turnaround was pointed out. Exactly one year later, in BISE 4/2009, the responsibility of our discipline for our planet and the threat of a worldwide resource crisis were discussed. The editorial 1/2012 and the corresponding special focus issue focused on "Smart Grids" and their relevance for the energy turnaround. All in all, we can see that the international community was aware of the energy turnaround's relevance even before the Fukushima reactor disaster. But this is not sufficient. Rather, to ensure the success of the energy turnaround it is essential that single countries do not indulge in blind activism. This is especially important because the aims associated with the energy turnaround, such as an increased use of regenerative energies, will lead to major challenges (e.g., it is Germany's goal to increase the use of regenerative energies from 20 % in 2011 to at least 35 % in 2020). Some of these challenges are briefly mentioned in the following:• The yield of renewable energies is often difficult to plan and control -e.g., wind and solar energy can only be extracted during suitable weather conditions. This frequently leads to a mismatch between the times of supply and demand.• These supply risks require energy st...

Predictive Maintenance on the Energy Distribution Grid–Design and Evaluation of a Digital Industrial Platform in the Context of a Smart Service System

“…A major strategy to tackle the amount of carbon dioxide emitted into the atmosphere is to shift the generation of electrical energy from resources outputting greenhouse gases to renewables, e.g., wind power and photovoltaic. In the German energy market, this transition is known as the Energy Turnaround [1]. In comparison to fossil energy sources, renewables are distributed and intermittent, forcing the distribution grid to change: The formerly unidirectional and centralized distribution grid has to become bidirectional and highly decentralized [2].…”

“…While reactive maintenance operates assets to the point of failure or required maintenance activities, preventive maintenance utilizes intervals based on, e.g., operating hours before assets are checked, and critical components replaced [8]. Predictive maintenance 1 utilizes real-time and historical condition data to monitor the current status of an asset permanently and predicts its remaining lifetime [10].…”