ERIGrid Holistic Test Description for Validating Cyber-Physical Energy Systems

Smart energy solutions aim to modify and optimise the operation of existing energy infrastructure. Such cyber-physical technology must be mature before deployment to the actual infrastructure, and competitive solutions will have to be compliant to standards still under development. Achieving this technology readiness and harmonisation requires reproducible experiments and appropriately realistic testing environments. Such testbeds for multi-domain cyber-physical experiments are complex in and of themselves. This work addresses a method for the scoping and design of experiments where both testbed and solution each require detailed expertise. This empirical work first revisited present test description approaches, developed a newdescription method for cyber-physical energy systems testing, and matured it by means of user involvement. The new Holistic Test Description (HTD) method facilitates the conception, deconstruction and reproduction of complex experimental designs in the domains of cyber-physical energy systems. This work develops the background and motivation, offers a guideline and examples to the proposed approach, and summarises experience from three years of its application.This work received funding in the European Community’s Horizon 2020 Program (H2020/2014–2020) under project “ERIGrid” (Grant Agreement No. 654113)

Contributions to virtual reality

153 p.The thesis contributes in three Virtual Reality areas: ¿ Visual perception: a calibration algorithm is proposed to estimate stereo projection parameters in head-mounted displays, so that correct shapes and distances can be perceived, and calibration and control procedures are proposed to obtain desired accommodation stimuli at different virtual distances.¿ Immersive scenarios: the thesis analyzes several use cases demanding varying degrees of immersion and special, innovative visualization solutions are proposed to fulfil their requirements. Contributions focus on machinery simulators, weather radar volumetric visualization and manual arc welding simulation.¿ Ubiquitous visualization: contributions are presented to scenarios where users access interactive 3D applications remotely. The thesis follows the evolution of Web3D standards and technologies to propose original visualization solutions for volume rendering of weather radar data, e-learning on energy efficiency, virtual e-commerce and visual product configurators

Grey-box Modelling of a Household Refrigeration Unit Using Time Series Data in Application to Demand Side Management

This paper describes the application of stochastic grey-box modeling to identify electrical power consumption-to-temperature models of a domestic freezer using experimental measurements. The models are formulated using stochastic differential equations (SDEs), estimated by maximum likelihood estimation (MLE), validated through the model residuals analysis and cross-validated to detect model over-fitting. A nonlinear model based on the reversed Carnot cycle is also presented and included in the modeling performance analysis. As an application of the models, we apply model predictive control (MPC) to shift the electricity consumption of a freezer in demand response experiments, thereby addressing the model selection problem also from the application point of view and showing in an experimental context the ability of MPC to exploit the freezer as a demand side resource (DSR).Comment: Submitted to Sustainable Energy Grids and Networks (SEGAN). Accepted for publicatio

Crossing Roads of Federated Learning and Smart Grids: Overview, Challenges, and Perspectives

Consumer's privacy is a main concern in Smart Grids (SGs) due to the sensitivity of energy data, particularly when used to train machine learning models for different services. These data-driven models often require huge amounts of data to achieve acceptable performance leading in most cases to risks of privacy leakage. By pushing the training to the edge, Federated Learning (FL) offers a good compromise between privacy preservation and the predictive performance of these models. The current paper presents an overview of FL applications in SGs while discussing their advantages and drawbacks, mainly in load forecasting, electric vehicles, fault diagnoses, load disaggregation and renewable energies. In addition, an analysis of main design trends and possible taxonomies is provided considering data partitioning, the communication topology, and security mechanisms. Towards the end, an overview of main challenges facing this technology and potential future directions is presented

Digital Twins based Day-ahead Integrated Energy System Scheduling under Load and Renewable Energy Uncertainties

By constructing digital twins (DT) of an integrated energy system (IES), one can benefit from DT’s predictive capabilities to improve coordinations among various energy converters, hence enhancing energy efficiency and cost saving.energy efficiency, cost savings and carbon emission reduction. This paper is motivated by the fact that practical IESs suffer from multiple uncertainty sources, and complicated surrounding environment. To address this problem, a novel DT-based day-ahead scheduling method is proposed. The physical IES is modelled as a multi-vector energy system in its virtual space that interacts with the physical IES to manipulate its operations. A deep neural network is trained to make statistical cost-saving scheduling by learning from both historical forecasting errors and day-ahead forecasts. Case studies of IESs show that the proposed DT-based method is able to reduce the operating cost of IES by 63.5%, comparing to the existing forecast-based scheduling methods.much lower long-term operating costs than those of existing forecast-based scheduling methods. It is also found that both electric vehicles and thermal energy storages play proactive roles in the proposed method, highlighting their importance in future energy system integration and decarbonisation

Adaptive Control of Resource Flow to Optimize Construction Work and Cash Flow via Online Deep Reinforcement Learning

Due to complexity and dynamics of construction work, resource, and cash flows, poor management of them usually leads to time and cost overruns, bankruptcy, even project failure. Existing approaches in construction failed to achieve optimal control of resource flow in a dynamic environment with uncertainty. Therefore, this paper introducess a model and method to adaptive control the resource flows to optimize the work and cash flows of construction projects. First, a mathematical model based on a partially observable Markov decision process is established to formulate the complex interactions of construction work, resource, and cash flows as well as uncertainty and variability of diverse influence factors. Meanwhile, to efficiently find the optimal solutions, a deep reinforcement learning (DRL) based method is introduced to realize the continuous adaptive optimal control of labor and material flows, thereby optimizing the work and cash flows. To assist the training process of DRL, a simulator based on discrete event simulation is also developed to mimic the dynamic features and external environments of a project. Experiments in simulated scenarios illustrate that our method outperforms the vanilla empirical method and genetic algorithm, possesses remarkable capability in diverse projects and external environments, and a hybrid agent of DRL and empirical method leads to the best result. This paper contributes to adaptive control and optimization of coupled work, resource, and cash flows, and may serve as a step stone for adopting DRL technology in construction project management

Towards soundscape fingerprinting: development, analysis and assessment of underlying acoustic dimensions to describe acoustic environments

Soundscape according to the definition in ISO 12913-1 describes an acoustic environment as perceived by humans in context. In order to be able to assess a soundscape holistically, the components acoustic environment, person and context should be described sufficiently to enable triangulation. Person-based soundscape assessment has been the subject of extensive research over the past decades to date, leading to a good understanding of the main emotional dimensions. On the acoustic side, e.g., in modeling emotional responses by acoustic features, parameters describing loudness are widely used, also from the point of view of legal regulations. These parameters are often complemented by established psychoacoustic measures. However, it is unknown to what extent these parameters are suitable to adequately describe and compare acoustic environments for hypotheses concerning humans. The presented dissertation aims to contribute to this field by means of an exploratory, empirical, and data-based approach. First, the general requirements of the aim – the description of acoustic environments – are defined and accompanied with concepts and findings from current research areas. Subsequently a methodology is developed that allows for the identification of underlying acoustic dimensions on the basis of empirical observational data of real world acoustic environments by means of multivariate statistical methods. It contains considerations on the physical sound field, the human auditory system, as well as appropriate signal processing techniques. The methodology is then applied to an exemplary extensive dataset of various Ambisonics soundscape recordings. The resulting expressions of the acoustic dimensions are evaluated and discussed with respect to plausibility and perceptual consistency. Finally, two application examples are presented to further validate the methodology and to test the applicability of acoustic dimensions in concrete research scenarios. It was found that the presented methodology is suitable to identify dimensions for the description of acoustic environments. Furthermore, the dimensions found form a suitable basis for further soundscape analyses.Soundscape (nach ISO 12913-1) beschreibt eine akustische Umgebung, wie sie von Menschen im Kontext wahrgenommen wird. Eine ganzheitliche Beurteilung einer Soundscape wird demnach durch Triangulation der Aspekte akustische Umgebung, Person und Kontext hergestellt. Die personenbezogene Bewertung von Soundscapes war und ist bis heute Gegenstand umfangreicher Forschungsarbeiten, die zu einem weitreichendem Verständnis der wichtigsten emotionalen Dimensionen geführt haben. Auf der akustischen Seite sind Parameter weit verbreitet, die die Lautstärke beschreiben. Ergänzt werden diese Parameter oft durch etablierte psychoakustische Größen. Unbekannt ist jedoch, inwieweit diese (psycho-)akustischen Parameter tatsächlich geeignet sind, Soundscapes zu beschreiben und zu vergleichen hinsichtlich den Menschen betreffender Hypothesen. Hierzu soll diese Dissertation einen Beitrag leisten. Der dabei verfolgte Ansatz ist explorativ, empirisch und datenbasiert. Zunächst werden Anforderungen an das Ziel – die Beschreibung akustischer Umgebungen – definiert und mit Konzepten aus aktuellen Forschungsgebieten ergänzt. Anschließend wird eine Methodik entwickelt, die es erlaubt, fundamentale akustische Dimensionen zu identifizieren auf der Basis empirischer Beobachtungsdaten realer akustischer Umgebungen und mit Hilfe multivariater statistischer Methoden. Sie enthält Überlegungen zum physikalischen Schallfeld, zur menschlichen Hörwahrnehmung sowie zu geeigneten Signalverarbeitungstechniken. Die Methodik wird anschließend auf einen beispielhaften Datensatz von Ambisonics Soundscape-Aufnahmen angewandt. Die resultierenden akustischen Dimensionen werden hinsichtlich ihrer Plausibilität und wahrnehmungsbezogenen Konsistenz diskutiert. Schließlich werden zwei Anwendungsbeispiele vorgestellt, um die Methodik weiter zu validieren und um die Anwendbarkeit der akustischen Dimensionen in konkreten Forschungsszenarien zu testen. Hierbei kann festgestellt werden, dass die gefundenen Dimensionen einen hohen Grad an Varianz akustischer Umgebungen erklären können und gut interpretierbar sind. Sie bilden somit eine geeignete Grundlage für die hier dargestellte Analyse von Soundscapes. Die Methodik ist dabei variabel erweiterbar, sodass vielfältige Anwendungen und Forschungsarbeiten bzgl. akustischer Umgebungen ermöglicht werden