Abstract. Wake steering models for control purposes are typically based on analytical wake descriptions tuned to match experimental or numerical data. This study explores whether a data-driven surrogate model with a high degree of physical interpretation can accurately describe the redirected wake. A linear model trained with large-eddy-simulation data estimates wake parameters such as deficit, center location and curliness from measurable inflow and turbine variables. These wake parameters are then used to generate vertical cross-sections of the wake at desired downstream locations. In a validation considering eight boundary layers ranging from neutral to stable conditions, the far wake's trajectory, curl and available power are accurately estimated. A significant improvement in accuracy is shown in a benchmark study against two analytical wake models, especially under derated operating conditions and stable atmospheric stratifications. Even though the results are not directly generalizable to all atmospheric conditions, locations or turbine types, the outcome of this study is encouraging.

There is a need for distributed PV generation location dataData on distributed PV installations can be difficult to access for researchers. Aerial or satellite images are a potential source of data, but manual identification is time prohibitive. Computer vision approaches that employ Neural Networks and Deep Learning may automate the identification of PV from images.

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Energy system modeling is essential in analyzing present and future system configurations motivated by the energy transition. Energy models need various input data sets at different scales, including detailed information about energy generation and transport infrastructure. However, accessing such data sets is not straightforward and often restricted, especially for energy infrastructure data. We present a detection model for the automatic recognition of pipeline pathways using a Convolutional Neural Network (CNN) to address this lack of energy infrastructure data sets. The model was trained with historical low-resolution satellite images of the construction phase of British gas transport pipelines, made with the Landsat 5 Thematic Mapper instrument. The satellite images have been automatically labeled with the help of high-resolution pipeline route data provided by the respective Transmission System Operator (TSO). We have used data augmentation on the training data and trained our model with four different initial learning rates. The models trained with the different learning rates have been validated with 5-fold cross-validation using the Intersection over Union (IoU) metric. We show that our model can reliably identify pipeline pathways despite the comparably low resolution of the used satellite images. Further, we have successfully tested the model’s capability in other geographic regions by deploying satellite images of the NEL pipeline in Northern Germany.

Wicked problems occur when decision-makers face constant change or unprecedented challenges and when uncertainty, complexity, and stakeholder divergence are high. We shed light on wicked problems in the German energy transition. Our methods consist of a multiple-case study and comparative multi-criteria analysis, utilising the wicked problems theoretical framework introduced by Horst Rittel and Melvin Webber (1973). Based on four exemplary cases, our research covers four core energy transition sectors: energy supply (developing onshore wind power), heating/cooling (using shallow geothermal energy systems), transport (decarbonising the transport sector), and industry (decarbonising the chemical industry sector). Cross-case results illustrate where and how the 10-point frame of wicked problems manifests in the German energy transition. We do not argue that the German energy transition is inherently wicked, yet we stress the need to consider potentially wicked facets of energy transition challenges. Our results show that the four cases exhibit more wicked tendencies in the governance domain than in the technical dimension. All cases exhibit wicked facets in the governance dimension, given strong normative assumptions, value divergence, and complex governance structures with a plurality of actors. From a technical perspective, the four cases still exhibit some wicked tendencies, e.g. raw material provision, skilled workforce, and waste management. The cases differ in technology maturity, state of knowledge, and degree of policy output and regulations. In applying the wickedness lens, we acknowledge that energy transition problems cannot be solved merely by technical measures but need to be tamed. Our work reflects which challenges and main barriers pertain to the four cases of the German energy transition. Understanding the elements of wickedness in a specific problem in the first step offers insights for addressing and managing these challenges in the next step.