Exemplifying Conflict Resolution in Multi-Objective Smart Micro-Grids

Distributed autonomic management systems following contradictory objectives raise difficult design challenges. We proposed a generic architecture to address this concern and exemplified it via manager integration solutions for multi-objective micro-grids (low-tension networks of the size of a district). This demo showcases some of these sample implementations via the MisTiGriD simulation platform with the aim of inspiring designers facing similar challenges

Generic architectures for open, multi-objective autonomic systems:application to smart micro-grids

Autonomic features, i.e. the capability of systems to manage themselves, are necessary to control complex systems, i.e. systems that are open, large scale, dynamic, comprise heterogeneous third-party sub-systems and follow multiple, sometimes conflicting objectives. In this thesis, we aim to provide generic reusable supports for designing complex autonomic systems. We propose a formalisation of management objectives, a generic architecture for designing adaptable multi-objective autonomic systems, and generic organisations integrating such autonomic systems. We apply our approach to the concrete case of smart micro-grids which is a relevant example of such complexity. We present a simulation platform we developped and illustrate our approach via several simulation scenarios

Digital sustainability and entrepreneurship: How digital innovations are helping tackle climate change and sustainable development

Ministry of Education, Singapore under its Academic Research Funding Tier

Rules for Watt?:Designing Appropriate Governance Arrangements for the Introduction of Smart Grids

The increasing generation of electricity from renewable energy sources like wind and sun furthers the transition to a sustainable energy system, but at the same time challenges the operation and management of the electricity grid. Smart grids are considered a sustainable and energy-efficient solution to this challenge because they can facilitate the integration of electricity from intermitted renewable energy sources and the accommodation of more fluctuating demand patterns in the distribution grid. The PhD thesis aims to contribute to the improvement of smart grid introduction in practice. Smart grids are considered essential for the Dutch energy transition, but decision-making in energy planning and on smart grid introduction is rather slow and time-consuming. To improve the introduction of smart grids in the Netherlands, this dissertation focusses on the institutional side of decision-making practices, and specifically on the ‘rules of the game’ governing multi-stakeholder local energy planning processes. The research is guided by the research question ‘how can local governance on the introduction of smart grids be improved?’ To address this question, first, governance arrangements inherent to decision-making on the introduction of smart grids in Dutch city districts are studied empirically. This led to three overarching findings: (1) efficiency in local energy planning on the introduction of smart grids is low; (2) there is mostly a lack of residents’ participation in the local planning process of their city district’s energy infrastructure; and (3) several rules-in-use are disabling local energy planning as well as are often conflicting with (experimental) rules-in-form. Based on this, second, two heuristics are developed that can facilitate the introduction of smart grids in local settings: an institutional architecture (of institutional arrangements) and a process architecture (of decision-making functionality). The two heuristics are not only suitable for the introduction of smart grids in Dutch local settings, but also for the realization of additional (integrated) smart energy infrastructures in different contexts

Machine Learning in Sensors and Imaging

Machine learning is extending its applications in various fields, such as image processing, the Internet of Things, user interface, big data, manufacturing, management, etc. As data are required to build machine learning networks, sensors are one of the most important technologies. In addition, machine learning networks can contribute to the improvement in sensor performance and the creation of new sensor applications. This Special Issue addresses all types of machine learning applications related to sensors and imaging. It covers computer vision-based control, activity recognition, fuzzy label classification, failure classification, motor temperature estimation, the camera calibration of intelligent vehicles, error detection, color prior model, compressive sensing, wildfire risk assessment, shelf auditing, forest-growing stem volume estimation, road management, image denoising, and touchscreens