2020, Braun, Tobias, Waechter, Matthias, Peinke, Joachim, Guhr, Thomas

Wind farms can be regarded as complex systems that are, on the one hand, coupled to the nonlinear, stochastic characteristics of weather and, on the other hand, strongly influenced by supervisory control mechanisms. One crucial problem in this context today is the predictability of wind energy as an intermittent renewable resource with additional non-stationary nature. In this context, we analyze the power time series measured in an offshore wind farm for a total period of one year with a time resolution of 10 min. Applying detrended fluctuation analysis, we characterize the autocorrelation of power time series and find a Hurst exponent in the persistent regime with crossover behavior. To enrich the modeling perspective of complex large wind energy systems, we develop a stochastic reduced-form model of power time series. The observed transitions between two dominating power generation phases are reflected by a bistable deterministic component, while correlated stochastic fluctuations account for the identified persistence. The model succeeds to qualitatively reproduce several empirical characteristics such as the autocorrelation function and the bimodal probability density function. © 2020 Author(s).

2018, Wohland, Jan, Witthaut, Dirk, Schleussner, Carl-Friedrich

The mitigation of climate change requires fast reductions in greenhouse gas emissions and calls for fundamental transitions of energy systems. In most places, the increased exploitation of variable renewable sources (wind and solar) forms the backbone of these transitions. To remain consistent with the Paris Agreement temperature goals, negative emission technologies will likely be needed to achieve net zero emissions in the second half of the century. In integrated assessment models, negative emissions are typically realized through land-based approaches. However, due to their coarse temporal and spatial resolution, such models might underestimate the potential of decentrally deployable and flexible technologies such as Direct Air Capture (DAC). Based on validated high-resolution power generation time series, we show that DAC can extract CO2 from the atmosphere and facilitate the integration of variable renewables at the same time. It is a promising flexibility provider as it can be ramped within minutes. Our results show that negative emissions of up to 500 Mt CO2/year in Europe may be achievable by using renewable excess energy only. Electricity systems with high shares of volatile renewables will induce excess generation events during which electricity is cheap thereby lowering the operational costs of DAC. If investment costs can be sufficiently reduced, this may render very energy intensive but highly flexible technologies such as DAC viable.