Abstract
While the transmission system benefits from a high observability, the distribution system has a relatively low level of observability. This problem is already being addressed with the deployment of smart meters, in an effort to make the smart grid concept a reality. Nevertheless, as observability increases, so too does the volume of data, which makes the development of advanced software tools a very important subject. In this paper, the application of image analysis techniques to a low voltage grid is explored, by converting voltage data into an image format, using a cognitive network to evaluate and cluster grid operating modes. The proposed method is applied to a 33-bus low voltage grid to evaluate voltage profiles at each bus and the associated technical limits (voltage limits alarms).

Abstract
Urban wastewater sector is being pushed to optimize processes in order to reduce energy consumption without compromising its quality standards. Energy costs can represent a significant share of the global operational costs (between 50% and 60%) in an intensive energy consumer. Pumping is the largest consumer of electrical energy in a wastewater treatment plant. Thus, the optimal control of pump units can help the utilities to decrease operational costs. This work describes an innovative predictive control policy for wastewater variable-frequency pumps that minimize electrical energy consumption, considering uncertainty forecasts for wastewater intake rate and information collected by sensors accessible through the Supervisory Control and Data Acquisition system. The proposed control method combines statistical learning (regression and predictive models) and deep reinforcement learning (Proximal Policy Optimization). The following main original contributions are produced: (i) model-free and data-driven predictive control; (ii) control philosophy focused on operating the tank with a variable wastewater set-point level; (iii) use of supervised learning to generate synthetic data for pre-training the reinforcement learning policy, without the need to physically interact with the system. The results for a real case-study during 90 days show a 16.7% decrease in electrical energy consumption while still achieving a 97% reduction in the number of alarms (tank level above 7.2 m) when compared with the current operating scenario (operating with a fixed set-point level). The numerical analysis showed that the proposed data-driven method is able to explore the trade-off between number of alarms and consumption minimization, offering different options to decision-makers.

Abstract
The increasing penetration of Distributed Energy Resources is changing the energy system by empowering consumers with the capacity to generate the electrical energy they need and sell its excess. This trend follows the EU strategy towards increasing competition and flexibility on the electricity market, as well as pushing the role of customers, expanding their rights and their involvement in energy communities (ECMs). Peer-to-Peer (P2P) energy markets appear as one of the possible solutions to accomplish these goals by providing direct energy trading between peers. Although P2P are being extensively addressed in the literature (e.g., market structures and platforms, experimental projects), few works offer a broad perspective of the different aspects involved in the actual implementation of these structures, as well as the real benefits that this type of markets can have for the players and for the system itself. This paper reviews business models related with ECMs and P2P markets, and the system benefits and main regulatory issues.

Abstract

Abstract
This paper presents the study carried out in the Electric Power Systems Project (EPSP) of the Electrical Engineering course, by a student's team, using PBL, under a protocol between the Law Court and the Instituto Superior de Engenharia de Coimbra (ISEC / IPC). The purpose of the Electric Power Systems Project is to involve students in a project team, aiming to develop and test a system with a specific function, using concepts and technologies in the field of Electric Power Systems. Upon completing the course, students should demonstrate autonomy in identifying and analysing problems and propose, implement and test the specific solutions. In this work, the Project Based Learning (PBL) methodology was used. The Problem to be solved was presented to the students, by the Professors. The aim of the students was to provide a global framework for the problem of road lighting in Portugal, identifying the technologies used, existing legislation, national plans for improving energy efficiency in the sector, identify the most efficient technologies, learn how to work with software for simulation, propose a solution to the specific problem and justify the solution from a technical and economic point of view.

Abstract
Over the last years, there has been an increase in hearing-impaired students who use sign language as their main form of communication attending higher education institutions around the world. The knowledge that their comprehension of texts is reduced due to sentence structure differences causes a need for more solutions to improve communication and support students in environments where they are unable to be accompanied by sign interpreters. This article details the improvements and current structure of the VirtualSign platform, a bidirectional sign language to text translation tool that has been in development since 2015. The platform is divided into two main parts, sign to text and text to sign, and both components are described and explained. The solution has received positive feedback on several tests and a pilot experiment, and is being developed with partnerships with sign interpreters from six different European countries. Some planned improvements and future functionalities for the tool are also mentioned and detailed.