Electricity production and distribution are facing major changes, namely the production is shifting from traditional towards renewable sources while the consumption in the low voltage grid is expected to grow significantly due to the transition to electrical vehicles. The increasing share of renewable energy resources and electrical vehicles increases the unpredictability and variability of the electricity networks in recent years, which brings many technical challenges in ensuring network reliability. In this respect, accurate knowledge of the distribution network state is the first prerequisite for quality management. To address this challenge we focused on the development of the distribution network state estimators, with special attention given to state estimator robustness and insensitivity to bad data present in the input set of measurements. Based on numerical solution algorithms, we evaluated the proposed estimators for different measurement configurations on reference IEEE networks. We also developed a sensitivity model of the considered state estimators to small model parameters errors and small measurement errors. We published a book as part of the Springer Briefs series that describes the design and implementation of a three-phase state estimation that is suitable for power-distribution networks. The book is among the top used publications (more than 400 payable downloads in a year) on SpringerLink that concern one or more of the United Nations Sustainable Development Goals.
F.06 Development of a new product
COBISS.SI-ID: 32983591Several members of the programme group are habilitated at the Jozef Stefan Postgraduate School (JSIPS) where they are lecturing a number of courses and supervising students at the master and doctoral levels. They incorporate new knowledge, acquired in national and international research projects, in the individual courses and transfer it to postgraduate students trough teaching and supervising activities. At JSIPS under the supervision of Mihael Mohorčič, a young researcher Klemen Bregar completed his doctoral studies in 2019 with the dissertation entitled "Modelling the effects of indoor environments on localization with ultra-wideband radio signals", and Matevž Vučnik in 2020 with the dissertation entitled "Streamlining the development of wireless embedded systems using continuous integration", co-supervised by Carolina Fortuna. In the reporting period also the following mentorships started or continued from the previous period: - Mihael Mohorčič to doctoral students Uroš Platiše and Gregor Cerar (young researcher) and to a master student Ivan Boškov, - Aleš Švigelj to a doctoral student Tadeja Saje, - Andrej Hrovat to a doctoral student Teodora Kocevska, - Carolina Fortuna to a doctoral student and young researcher Blaž Bertalanič. Members of the programme group are also regularly invited to the evaluation boards for doctoral topics, dissertations and defenses at JSIPS as well as other national (e.g. Faculty of Electrical Engineering, University of Ljubljana) and international (e.g. Trinity College Dublin) institutions.
D.09 Tutoring for postgraduate students
COBISS.SI-ID: 300770816In 2017 we successfully concluded the European project NRG-5 from the 5G-PPP Phase 2, where we participated in investigating the suitability of the 5G communication network to serve the energy vertical domain, in particular to provide optimal communications of the energy grid services in terms of supporting massive smart metering and very low latency for control and fault localization. We also participated in the piloting of auto-configuration of smart metering devices to support multi-tenancy in mobility scenarios requiring high reliability and very low latency. Towards the end of the project Carolina Fortuna from JSI and John Davies from British Telecom co-edited a book entitled "Internet of Things: From Data to Insight", that was published by Wiley. Written by experts in the field, mostly partners of NRG-5 project, this book addresses the IoT technology stack, from connectivity through data platforms to end-user case studies, and considers the tradeoffs between business needs and data security and privacy throughout. There is a particular emphasis on data processing technologies that enable the extraction of actionable insights from data to inform improved decision making. These include artificial intelligence techniques such as stream processing, deep learning and knowledge graphs, as well as data interoperability and the key aspects of privacy, security and trust. Additional aspects covered include: creating and supporting IoT ecosystems; edge computing; data mining of sensor datasets; and crowd-sourcing, amongst others.
C.01 Editorial board of a foreign/international collection of papers/book
COBISS.SI-ID: 33305895With the European Space Agency (ESA) we extended the project of long-term Ka/Q-band propagation measurement using our own developed 4-channel Alphasat beacon receiver station until 2020 and in this period we achieved the highest data availability (99.6%) among all the groups involved in signal measurements from the Alphasat satellite. With these measurements and data we expanded the database of measurements for the analysis of signal attenuation due to atmospheric impairments, and we used them for satellite propagation channel modelling and for verification of the simulator developed by the Luxembourg company HiTEC. With measurements and results of analyses we participated in the international European Experimental Group ASAPE (Group of the AlphaSat Aldo Paraboni propagation Experimenters) and the network of ASALASCA experts, while we also contributed appropriately prepared data to the ITU-R database.
F.16 Improvements to an existing information system/databases
COBISS.SI-ID: 13633027We have implemented radio frequency (RF) ray tracing algorithms on multi-core platforms and as a remote cloud service. The solution enables the use of accurate deterministic characterization of the radio environment in telecommunication problems in which on-site computational resources are limited. Through a remote cloud service, the calculation of the radio environment can be accessed by a wider range of potential users, from developers of various web and mobile applications to wireless network planners and network operators. We established a test infrastructure in the cloud, developed and tested all the necessary software components, implemented a mechanism for compiling and installing the system, and improved and analyzed the operation of algorithms for the needs of parallel implementation. For the needs of faster and comprehensive testing, we also created an independent application Signal3D (http://e6.ijs.si/tools), which simplifies the creation of input simulation models and the conversion and adjustment of parameters of existing geometries.
F.11 Development of a new service
COBISS.SI-ID: 32211239