João Paulo Viana

Abstract
In the power system decarbonization roadmap, novel grid management tools and market mechanisms are fundamental to solving technical problems concerning renewable energy forecast uncertainty. This work proposes a predictive algorithm for procurement of grid flexibility by the system operator (SO), which combines the SO flexible assets with active and reactive power short-term flexibility markets. The goal is to reduce the cognitive load of the human operator when analyzing multiple flexibility options and trajectories for the forecasted load/RES and create a human-in-the-loop approach for balancing risk, stakes, and cost. This work also formulates the decision problem into several steps where the operator must decide to book flexibility now or wait for the next forecast update (time-to-decide method), considering that flexibility (availability) price may increase with a lower notification time. Numerical results obtained for a public MV grid (Oberrhein) show that the time-to-decide method improves up to 22% a performance indicator related to a cost-loss matrix, compared to the option of booking the flexibility now at a lower price and without waiting for a forecast update.

Abstract
The increasing integration of renewable energy sources (RES) at different voltage levels of the distribution grid has led to technical challenges, namely voltage and congestion problems. Conversely, the integration of new Distributed Energy Resources (DER) provides the necessary flexibility to accommodate higher RES integration levels. This work describes the development of innovative functional modules, based on optimal power flow calculations and grid forecasting, dedicated to the predictive management of the distribution grid considering DER flexibility, which are integrated into a commercial SCADA/DMS solution. © The Institution of Engineering and Technology 2023.

Abstract
Network human operators' decision-making during grid outages requires significant attention and the ability to perceive real-time feedback from multiple information sources to minimize the number of control actions required to restore service, while maintaining the system and people safety. Data-driven event and alarm management have the potential to reduce human operator cognitive burden. However, the high complexity of events, the data semantics, and the large variety of equipment and technologies are key barriers for the application of Artificial Intelligence (AI) to raw SCADA data. In this context, this paper proposes a methodology to convert a large volume of alarm events into data mining terminology, creating the conditions for the application of modern AI techniques to alarm data. Moreover, this work also proposes two novel data-driven applications based on SCADA data: (i) identification of anomalous behaviors regarding the performance of the protection relays of primary substations, during circuit breaker tripping alarms in High Voltage (HV) and Medium Voltage (MV) lines; (ii) unsupervised learning to cluster similar events in HV line panels, classify new event logs based on the obtained clusters and membership grade with a control parameter that helps to identify rare events. Important aspects associated with data handling and pre-processing are also covered. The results for real data from a Distribution System Operator (DSO) showed: (i) that the proposed method can detect unexpected relay pickup events, e.g., one substation with nearly 41% of the circuit breaker alarms had an 'atypical' event in their context (revealed an overlooked problem on the electrification of a protection relay); (ii) capability to automatically detect and group issues into specific clusters, e.g., SF6 low-pressure alarms and blocks with abnormal profiles caused by event time-delay problems.

Abstract
Energy efficiency in buildings can be enhanced by several actions: encouraging users to comprehend and then adopt more energy-efficient behaviors; aiding building managers in maximizing energy savings; and using automation to optimize energy consumption, generation, and storage of controllable and flexible devices without compromising comfort levels and indoor air-quality parameters. This paper proposes an integrated Information and communications technology (ICT) based platform addressing all these factors. The gamification platform is embedded in the ICT platform along with an interactive energy management system, which aids interested stakeholders in optimizing "when and at which rate" energy should be buffered and consumed, with several advantages, such as reducing peak load, maximizing local renewable energy consumption, and delivering more efficient use of the resources available in individual buildings or blocks of buildings. This system also interacts with an automation manager and a users' behavior predictor application. The work was developed in the Horizon 2020 FEEdBACk (Fostering Energy Efficiency and BehAvioral Change through ICT) project.

Abstract
The energy efficiency requirements of most energy-consuming sectors have increased recently in response to climate change. For buildings, this means targeting both facility managers and building users with the aim of identifying potential energy savings and encouraging more energy-responsible behaviors. The Information and Communication Technology (ICT) platform developed in Horizon 2020 FEEdBACk project intends to fulfill these goals by enabling the optimization of energy consumption, generation, and storage and control of flexible devices without compromising comfort levels and indoor air quality parameters. This work aims to demonstrate the real-world implementation and functionality of the ICT platform composed of Load Disaggregation, Net Load Forecast, Occupancy Forecast, Automation Manager, and Behavior Predictor applications. Particularly, the results obtained by individual applications during the test phase are presented alongside the specific metrics used to evaluate their performance.