On the socio-technical potential for onshore wind in Europe : a response to Enevoldsen et al. (2019), Energy Policy, 132, 1092-1100

Acknoweldgements: S.W. and J.S. received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (reFUEL, grant agreement No. 758149). J.L. and T.T. received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 715132).Peer reviewedPostprin

High-resolution large-scale onshore wind energy assessments: A review of potential definitions, methodologies and future research needs

The rapid uptake of renewable energy technologies in recent decades has increased the demand of energy researchers, policymakers and energy planners for reliable data on the spatial distribution of their costs and potentials. For onshore wind energy this has resulted in an active research field devoted to analysing these resources for regions, countries or globally. A particular thread of this research attempts to go beyond purely technical or spatial restrictions and determine the realistic, feasible or actual potential for wind energy. Motivated by these developments, this paper reviews methods and assumptions for analysing geographical, technical, economic and, finally, feasible onshore wind potentials. We address each of these potentials in turn, including aspects related to land eligibility criteria, energy meteorology, and technical developments of wind turbine characteristics such as power density, specific rotor power and spacing aspects. Economic aspects of potential assessments are central to future deployment and are discussed on a turbine and system level covering levelized costs depending on locations, and the system integration costs which are often overlooked in such analyses. Non-technical approaches include scenicness assessments of the landscape, constraints due to regulation or public opposition, expert and stakeholder workshops, willingness to pay/accept elicitations and socioeconomic cost-benefit studies. For each of these different potential estimations, the state of the art is critically discussed, with an attempt to derive best practice recommendations and highlight avenues for future research

Computing Technical Capacities in the European Entry-Exit Gas Market is NP-Hard

As a result of its liberalization, the European gas market is organized as an entry-exit system in order to decouple the trading and transport of natural gas. Roughly summarized, the gas market organization consists of four subsequent stages. First, the transmission system operator (TSO) is obliged to allocate so-called maximal technical capacities for the nodes of the network. Second, the TSO and the gas traders sign mid- to long-term capacity-right contracts, where the capacity is bounded above by the allocated technical capacities. These contracts are called bookings. Third, on a day-ahead basis, gas traders can nominate the amount of gas that they inject or withdraw from the network at entry and exit nodes, where the nominated amount is bounded above by the respective booking. Fourth and finally, the TSO has to operate the network such that the nominated amounts of gas can be transported. By signing the booking contract, the TSO guarantees that all possibly resulting nominations can indeed be transported. Consequently, maximal technical capacities have to satisfy that all nominations that comply with these technical capacities can be transported through the network. This leads to a highly challenging mathematical optimization problem. We consider the specific instantiations of this problem in which we assume capacitated linear as well as potential-based flow models. In this contribution, we formally introduce the problem of Computing Technical Capacities (CTC) and prove that it is NP-complete on trees and NP-hard in general. To this end, we first reduce the Subset Sum problem to CTC for the case of capacitated linear flows in trees. Afterward, we extend this result to CTC with potential-based flows and show that this problem is also NP-complete on trees by reducing it to the case of capacitated linear flow. Since the hardness results are obtained for the easiest case, i.e., on tree-shaped networks with capacitated linear as well as potential-based flows, this implies the hardness of CTC for more general graph classes

Power to Gas: Netzzugangsmodelle und Marktdesign

Das zukünftige Energiesystem ist durch einen hohen Anteil an erneuerbaren Energien (EE) geprägt, welche sowohl positive als auch negative Residuallasten nach sich ziehen. Unter dem Begriff „Power to gas“ versteht man die Wandlung von Wasser in Wasserstoff und Sauerstoff mittels Elektrolyse und unter Einsatz insbesondere regenerativ erzeugten Stroms. Dieser Wasserstoff kann beispielsweise im Verkehrssektor oder der chemischen Industrie direkt genutzt bzw. methanisiert werden. Der Nutzungspfad des Wasserstoffes bestimmt somit das zukünftig notwendige Markdesign. Als methanisierter Wasserstoff ist der bestehende Erdgasmarkt zu adressieren. Dieser Markt wird beschrieben und es wird auf weiterführende Literatur verwiesen. Für die Nutzung des Wasserstoffs im Verkehrssektor ist ein neues Marktdesign, welches sich an den bestehenden Netzzugangsmodellen orientiert, zu entwickeln. Die möglichen Modelle werden daher anhand ihrer Vor-und Nachteile miteinander verglichen und die Modelle je nach Entwicklungsphase der Wasserstoffinfrastruktur ausgewählt

Optimized electrolyzer operation: Employing forecasts of wind energy availability, hydrogen demand, and electricity prices

One of the main advantages of fuel cell based mobility over other sustainable mobility concepts is the flexible production of hydrogen via electrolysis. To date, it is unclear how electrolysis at hydrogen refueling stations should be operated in order to achieve the lowest possible costs despite the constraints of hydrogen demand. This study proposes and evaluates an intelligent operating strategy for electrolysis capable of exploiting times of low electricity prices while participating in the spot market and maximizing wind energy utilization when combined with a wind farm. This strategy is based on a simulation model considering imperfect forecasts (e.g. of wind availability or energy prices) and non-linear electrolyzer behavior. Results show that this approach reduces hydrogen production costs by up to 9.2% and increases wind energy utilization by up to 19%, respectively