Expert survey and classification of tools for modeling and simulating hybrid energy networks

Sector coupling is expected to play a key role in the decarbonization of the energy system by enabling the integration of decentralized renewable energy sources and unlocking hitherto unused synergies between generation, storage and consumption. Within this context, a transition towards hybrid energy networks (HENs), which couple power, heating/cooling and gas grids, is a necessary requirement to implement sector coupling on a large scale. However, this transition poses practical challenges, because the traditional domain-specific approaches struggle to cover all aspects of HENs. Methods and tools for conceptualization, system planning and design as well as system operation support exist for all involved domains, but their adaption or extension beyond the domain they were originally intended for is still a matter of research and development. Therefore, this work presents innovative tools for modeling and simulating HENs. A categorization of these tools is performed based on a clustering of their most relevant features. It is shown that this categorization has a strong correlation with the results of an independently carried out expert review of potential application areas. This good agreement is a strong indicator that the proposed classification categories can successfully capture and characterize the most important features of tools for HENs. Furthermore, it allows to provide a guideline for early adopters to understand which tools and methods best fit the requirements of their specific applications

Scientific drilling projects in ancient lakes: integrating geological and biological histories

Sedimentary sequences in ancient or long-lived lakes can reach several thousands of meters in thickness and often provide an unrivalled perspective of the lake's regional climatic, environmental, and biological history. Over the last few years, deep drilling projects in ancient lakes became increasingly multi- and interdisciplinary, as, among others, seismological, sedimentological, biogeochemical, climatic, environmental, paleontological, and evolutionary information can be obtained from sediment cores. However, these multi- and interdisciplinary projects pose several challenges. The scientists involved typically approach problems from different scientific perspectives and backgrounds, and setting up the program requires clear communication and the alignment of interests. One of the most challenging tasks, besides the actual drilling operation, is to link diverse datasets with varying resolution, data quality, and age uncertainties to answer interdisciplinary questions synthetically and coherently. These problems are especially relevant when secondary data, i.e., datasets obtained independently of the drilling operation, are incorporated in analyses. Nonetheless, the inclusion of secondary information, such as isotopic data from fossils found in outcrops or genetic data from extant species, may help to achieve synthetic answers. Recent technological and methodological advances in paleolimnology are likely to increase the possibilities of integrating secondary information, e.g., through molecular dating of molecular phylogenies. Some of the new approaches have started to revolutionize scientific drilling in ancient lakes, but at the same time, they also add a new layer of complexity to the generation and analysis of sediment core data. The enhanced opportunities presented by new scientific approaches to study the paleolimnological history of these lakes, therefore, come at the expense of higher logistic, communication, and analytical efforts. Here we review types of data that can be obtained in ancient lake drilling projects and the analytical approaches that can be applied to empirically and statistically link diverse datasets for creating an integrative perspective on geological and biological data. In doing so, we highlight strengths and potential weaknesses of new methods and analyses, and provide recommendations for future interdisciplinary deep drilling projects

Graz Griffins’ Solution to the European Robotics Challenges 2014

An important focus of current research in the field of Micro Aerial Vehicles (MAVs) is to increase the safety of their operation in general unstructured environments. An example of a real-world application is visual inspection of industry infrastructure, which can be greatly facilitated by autonomous multicopters. Currently, active research is pursued to improve real-time vision-based localization and navigation algorithms. In this context, the goal of Challenge 3 of the EuRoC 20144 Simulation Contest was a fair comparison of algorithms in a realistic setup which also respected the computational restrictions onboard an MAV. The evaluation separated the problem of autonomous navigation into four tasks: visual-inertial localization, visual-inertial mapping, control and state estimation, and trajectory planning. This EuRoC challenge attracted the participation of 21 important European institutions. This paper describes the solution of our team, the Graz Griffins, to all tasks of the challenge and presents the achieved results

Overview obstacle maps for obstacle aware navigation of autonomous drones

Achieving the autonomous deployment of aerial robots in unknown outdoor environments using only onboard computation is a challenging task. In this study, we have developed a solution to demonstrate the feasibility of autonomously deploying drones in unknown outdoor environments, with the main capability of providing an obstacle map of the area of interest in a short period of time. We focus on use cases where no obstacle maps are available beforehand, for instance, in search and rescue scenarios, and on increasing the autonomy of drones in such situations. Our vision‐based mapping approach consists of two separate steps. First, the drone performs an overview flight at a safe altitude acquiring overlapping nadir images, while creating a high‐quality sparse map of the environment by using a state‐of‐the‐art photogrammetry method. Second, this map is georeferenced, densified by fitting a mesh model and converted into an Octomap obstacle map, which can be continuously updated while performing a task of interest near the ground or in the vicinity of objects. The generation of the overview obstacle map is performed in almost real time on the onboard computer of the drone, a map of size 100 m 75 × m is created in ≈2.75 min, therefore, with enough time remaining for the drone to execute other tasks inside the area of interest during the same flight. We evaluate quantitatively the accuracy of the acquired map and the characteristics of the planned trajectories. We further demonstrate experimentally the safe navigation of the drone in an area mapped with our proposed approac

Automatic Wrapper Induction from Hidden-Web Sources with Domain Knowledge ABSTRACT

We present an original approach to the automatic induction of wrappers for sources of the hidden Web that does not need any human supervision. This approach heavily relies on some domain knowledge, expressed in a predefined form, for a given domain of interest. There are two parts in the understanding of a given service of the hidden Web: understanding the structure of its input and the way its output is presented. This amounts to understanding the structure of a given form and to relate its fields to concepts of the domain of interest, and to understanding where and how resulting records are represented in an HTML result page. For the former problem, we use a combination of heuristics and of probing with domain instances; for the latter, we use a supervised machine learning technique adapted to tree-like information on an automatic, imperfect, and imprecise, annotation using the domain knowledge. The result of these two steps is the possibility to automatically wrap a form as a standard Web service with a WSDL description. We implemented such a system and show experiments that demonstrate the validity and potential of this approach

Automatic thermal model identification and distributed optimisation for load shifting in city quarters

Buildings with floor heating or thermally activated building structures offer significant potential for shifting the thermal load and thus reduce peak demand for heating or cooling. This potential can be realised with the help of model predictive control (MPC) methods, provided that sufficiently descriptive mathematical models of the thermal characteristics of the individual thermal zones exist. Creating these by hand is infeasible for larger numbers of zones; instead, they must be identified automatically based on measurement data. In this paper an approach is presented that allows automatically identifying thermal models usable in MPC. The results show that the identified zone models are sufficiently accurate for the use in an MPC, with a mean average error below $1.5{\rm \; K}$ for the prediction of the zone temperatures. The identified zone models are then used in a distributed optimisation scheme that coordinates the individual zones and buildings of a city quarter to best support an energy hub by flattening the overall load profile. In a preliminary simulation study carried out for buildings with floor heating, the operating costs for heating in a winter month were reduced by approximately 9%. Therefore, it can be concluded that the proposed approach has a clear economic benefit