The increasing penetration of electrical vehicles (EVs), on the way to decarbonizing the transportation sector, presents several challenges and opportunities for the end users, the distribution grid, and the electricity markets. Uncontrollable EV charging may increase peak demand and impact the grid stability and reliability, especially in the case of non-interconnected microgrids such as the distribution grids of small islands. On the other hand, if EVs are considered as flexible loads and distributed storage, they may offer Vehicle to Grid (V2G) services and contribute to demand-side management through smart charging and discharging. In this work, we present a study on the penetration of EVs and the flexibility they may offer for services to the grid, using a genetic algorithm for optimum valley filling and peak shaving for the case of a non-interconnected island where the electricity demand is several times higher during the summer due to the influx of tourists. Test cases have been developed for various charging/discharging strategies and mobility patterns. Their results are discussed with respect to the current generating capacity of the island as well as the future case where part of the electricity demand will have to be met by renewable energy sources, such as photovoltaic plants, in order to minimize the island’s carbon footprint. Higher EV penetration, in the range of 20–25%, is enabled through smart charging strategies and V2G services, especially for load profiles with a large difference between the peak and low demands. However, the EV penetration and available flexibility is subject to the mobility needs and limited by the population and the size of the road network of the island itself rather than the grid needs and constraints. Limitations and challenges concerning efficient V2G services on a non-interconnected microgrid are identified. The results will be used in the design of a smart charging controller linked to the microgrid’s energy management system.

The shift towards distributed generation and microgrids has renewed the interest in forecasting algorithms and methods, which need to take into account the advances in information, metering and control technologies in order to address the challenges of forecasting problems. Technologies such as machine learning have been proven useful for short-term electricity load forecasting, especially for microgrids, as they can also take into account several types of historical data and can adapt to changes often encountered in small-scale systems and on a short time scale. In this paper, we present a flexible and easily customized modular toolbox, called Divinus, for electricity use profiling and forecasting in microgrids. Divinus may support a variety of machine learning algorithms for forecasting and profiling that can be used independently or combined. For demonstration purposes, we have implemented Self-Organizing Maps for profiling and k-Neighbors for forecasting. The testing of the platform was based on electricity consumption data of the Euripus campus of the National and Kapodistrian University of Athens in Evia, Greece, from January 2010 till March 2018. The tests that have been carried out so far show that the platform can be easily customized and the algorithms examined yield high accuracy and acceptable mean errors for the case of a university campus energy profile.

Short-Term Load Forecasting (STLF) is nowadays a crucial and integral part of the energy production procedure to the emerging technologies for demand side management. The numerous approaches and algorithms proposed take advantage of the advances in information, metering and control technologies to address the challenges of distributed generation and intermittent energy sources on the one hand and the electricity markets on the other. This paper describes a flexible and easily customized, modular toolbox, called Divinus, for electricity use profiling and forecasting in microgrids. Divinus supports algorithms for forecasting and profiling that can be used independently or combined and its architecture consists of several interconnected well-defined components where each one interacts directly with the other. In this work, we have implemented Self-Organizing Maps for profiling and k-Neighbors for forecasting. In order to test the functionalities of the platform, we used electricity consumption data of the TEISTE campus in Evia, Greece from January 2010 till March 2018. From the tests that have been carried out so far, we have observed that the proposed approach yields high accuracy and acceptable mean errors.

As load forecasting nowadays is a crucial and integral part of the energy production procedures a large number of forecasting methods has been pro- posed to address it

Nowadays, the ability to design teaching and learning tools and content in the form of innovative ICT based educational applications is an essential skill given the increasing time that people spend on mobile devices and the transformation of educational systems to accommodate both formal and non-formal education and align them with life – long learning and the increasing demand for distance education. Persuasive technologies and methodologies, such as gamification, have been adjusted and developed to conform with Learning Management Systems used for online education in order to foster the students' motivation and boost their performance. In this paper, we introduce a methodology for developing educational applications based on well-established behavioral and cognitive models making use of gamification elements. Guidelines for developing a gamified course are offered and instances of a sample gamified course offered on the Moodle platform are presented to illustrate the use of the available mechanics.

In this work we study applicability of Kalman filters as decision support for early warning and emergency response system for infectious diseases as CoVID-19. Here we use only the actual observations of new cases/deaths from epidemiological survey. We investigated the behavior of various time varying measurement driven models. We implement time varying Kalman filters. Preliminary results from Greece and Latvia showed that Kalman Filters can be used for short term forecasting of Co Vid-19cases. The mean percent absolute error may vary by model; some models give satisfactory results where the mean percent absolute error in new cases is of the order of 2%-5%. The mean absolute error in new deaths is of the order of 1-2 deaths. We propose the use of Kalman Filters for short term forecasting, i.e. next day, which can be a useful tool for improved crisis management at the points of entry to a country or hospitals.