Abstract
As AI algorithms thrive on data, SCADA would be considered a natural ground for Artificial Intelligence (AI) applications to be developed, translating that avalanche of information into meaningful and fast insights to human operators. However, presently, the high complexity of the events, the data semantics, the large variety of equipment and technologies translate into very few AI applications developed in SCADA. Aware of the enormous potential yet to be explored, E-REDES partnered with INESC TEC to experiment on the development of two novel AI applications based on SCADA data. The first tool, called Alarm2Insights, identifies anomalous behaviours regarding the performance of the protection functions associated with HV and MV line panels. The second tool, called EventProfiler, uses unsupervised learning to identify similar events (i.e., with similar log messages) in HV line panels, and supervised learning to classify new events into previously defined clusters and detect unique or rare events. Aspects associated to data handling and pre-processing are also discussed. The project's results show a very promising potential of applying AI to SCADA data, enhancing the role of the operator and support him in doing better and more informed decisions. © 2021 The Institution of Engineering and Technology.

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
Electric vehicles (EVs) are crucial in achieving a low-carbon transportation sector and can inherently offer demand-side flexibility by responding to price signals and incentives, yet real-world strategies to influence charging behavior remain limited. This paper combines bilevel optimization and causal machine learning as complementary tools to design and evaluate dynamic incentive schemes as part of a pilot project using a supermarket's EV charging station network. The bilevel model determines discount levels, while double machine learning quantifies the causal impact of these incentives on charging demand. The results indicate a marginal increase of 1.16 kW in charging demand for each one-percentage-point increase in discount. User response varies by hour and weekday, revealing treatment effect heterogeneity, insights that can inform business decision-making. While the two methods are applied independently, their combined use provides a framework for connecting optimization-based incentive design with data-driven causal evaluation. By isolating the impact of incentives from other drivers, the study sheds light on the potential of incentives to enhance demand-side flexibility in the electric mobility ecosystem.

Abstract
A fast response to faults in large-scale photovoltaic power plants (PVPPs), which can occur on hundreds of components like photovoltaic panels and inverters, is fundamental for maximizing energy generation and reliable system operation. This work proposes using a Graph Neural Network (GNN) combined with a digital twin for synthetic fault data scenario generation for fault location in PVPPs. It shows that GNN can adapt to system changes without requiring model retraining, thus offering a scalable solution for the real operating PVPPs, where some parts of the system may be disconnected for maintenance. The results for a real PVPP show the GNN outperforms baseline models, especially in larger topologies, achieving up to twice the accuracy in a fault location task. The GNN's adaptability to topology changes was tested on the simulated reconfigured systems. A decrease in performance was observed, and its value depends on the complexity of the original training topology. It can be mitigated by using several system reconfigurations in the training set.

Abstract
Climate change, geopolitical tensions, and decarbonization targets are bringing the resilience of the European electric power system to the forefront of discussion. Among various regulatory and technological solutions, voluntary demand response can help balance generation and demand during periods of energy scarcity or renewable energy generation surplus. This work presents an open data service called Interoperable Recommender that leverages publicly accessible data to calculate a country-specific operational balancing risk, providing actionable recommendations to empower citizens toward adaptive energy consumption, considering interconnections and local grid constraints. Using semantic interoperability, it enables third- party services to enhance energy management and customize applications to consumers. Real-world pilots in Portugal, Greece, and Croatia with over 300 consumers demonstrated the effectiveness of providing signals across diverse contexts. For instance, in Portugal, 7% of the hours included actionable recommendations, and metering data revealed a consumption decrease of 4% during periods when consumers were requested to lower consumption.

Abstract
The integration of electric vehicles is paramount to the electrification of the transport sector, supporting the energy transition. The charging process of electric vehicles can be perceived as a controllable load and targeted with price or incentive-based programs. Demand-side management can optimize charging station performance and integrate renewable energy generation through electrical energy storage. Data flowing through charging stations can be used in computational approaches to solve open challenges and create new services, such as a dynamic pricing strategy, where the charging tariff depends on operating conditions. This work presents a data-driven service that optimizes day-ahead charging tariffs with a bilevel optimization problem and develops a case study around a large-scale pilot. The impact of photovoltaics and battery storage on the dynamic pricing scheme was assessed. A dynamic pricing strategy was found to benefit self-consumption and self-sufficiency of the charging station, increasing over 4 percentage points in some cases.