Abstract
While the share of renewable energy in interconnected systems has been increasing steadily, in isolated systems it represents a bigger challenge. This paper presents a dispatch algorithm integrating thermal, wind, solar and hydro generation and storage for an isolated network, which allows maximizing renewable energy integration and reducing the share of thermal energy in the mix. The possibility of using the battery to provide spinning reserve is also considered. The algorithm was tested and validated using real data from the island of Madeira, Portugal. Results prove the robustness and flexibility of the algorithm, showing that a significant decrease in the thermal fraction is achievable, and that it is possible to accommodate an increase in renewable generation with minimal or no curtailment at all.

Abstract
System operators (SO) require Converted-Interfaced Renewable Energy Systems (CI-RES) power plants investors to provide demonstrative studies related to different operational performance capabilities and advanced system services provision to the grid. Typically, these studies rely on Original Equipment Manufacturer (OEM) simulation models for the power converters and CI-RES power plants control units. Such models might be unavailable to the SO due to confidentiality reasons and might present challenges in parametrization due to their complexity. Moreover, compatibility issues between simulation packages used by the SO and those utilized by the independent entity performing the studies creates additional difficulties. Hence, SO demand to power plant investors the proving of equivalent simulation models and resorting preferably to standardized open-source models. This work presents a methodology to derive an equivalent model of a CI-RES power plant using Generic Renewable Energy Models (GREM) in which the parameters identification is performed exploiting an Evolutionary Particle Swarm Optimization (EPSO) to capture the plant's dynamic behaviour at the Point of Interconnection (POI) in face of a set of reference network disturbances. Considering as Case-Study the integration of a PV-Battery Hybrid power plat into the electrical system of Terceira Island, the results demonstrate successful derivation of GREM parameters allowing the representation of the dynamic behaviour of the power plant in face of network disturbance events. © Energynautics GmbH.

Abstract
Advanced control, operation, and planning tools of electrical networks with ML are not straightforward. 110 experts were surveyed to show where and how ML algorithms could advance. This paper assesses this survey and research environment. Then, it develops an innovation roadmap that helps align our research community with a goal-oriented realisation of the opportunities that AI upholds. This paper finds that the R&D environment of system operators (and the surrounding research ecosystem) needs adaptation to enable faster developments with AI while maintaining high testing quality and safety. This roadmap serves system operators, academics, and labs advancing next-generation electrical network tools.

Abstract
Foundation models (FMs) currently dominate news headlines. They employ advanced deep learning architectures to extract structural information autonomously from vast datasets through self-supervision. The resulting rich representations of complex systems and dynamics can be applied to many downstream applications. Therefore, advances in FMs can find uses in electric power grids, challenged by the energy transition and climate change. This paper calls for the development of FMs for electric grids. We highlight their strengths and weaknesses amidst the challenges of a changing grid. It is argued that FMs learning from diverse grid data and topologies, which we call grid foundation models (GridFMs), could unlock transformative capabilities, pioneering a new approach to leveraging AI to redefine how we manage complexity and uncertainty in the electric grid. Finally, we discuss a practical implementation pathway and road map of a GridFM-v0, a first GridFM for power flow applications based on graph neural networks, and explore how various downstream use cases will benefit from this model and future GridFMs.

Abstract
This study aims to improve the accuracy of wind power generation forecasting by selecting the potential locations for weather stations, which serve as crucial data sources for wind predictions. The proposed method is based on using Shapley values. First, they are assigned to stations that are already available in the region based on their contribution to forecasting error. Second, the values are interpolated to cover the area of interest. We test the hypothesis that taking weather measurements in areas with negative Shapley values leads to a decrease in the error of forecasting the volume of wind power generation. We estimate the method's impact on forecasting error by using long short-term memory neural network and linear regression with quadratic penalization. The results of this proof-of-concept study indicate that it is possible to improve the short-term wind power forecasts using additional weather observations in the selected regions. The future research should be dedicated to the expansion of the case study area to other locations, including offshore power plants.

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.