WP1 focuses on data-driven and robust control methodologies, making significant progress during the reporting period. The primary output was Deliverable D1.1 summarizing the progress and methodological developments of the four doctoral candidates (DCs).
DC2 focused on Smart Energy Systems (SESs), particularly on addressing uncertainty and disturbances. Through a literature review, DC2 applied data-driven system identification using Koopman operator theory, which proved effective for capturing the nonlinear dynamics of grid-following inverters that connect renewable resources like solar and wind to the grid.
DC3 worked on active distribution networks with high penetration of inverter-based resources. DC3 developed a safe multi-agent reinforcement learning (MARL) approach for voltage regulation. Simulations demonstrated its effectiveness in maintaining voltage compliance while reducing power losses and improving system robustness.
DC4 concentrated on floating offshore wind turbines (FOWTs) in turbulent wind and platform-induced disturbances. DC4 developed an adaptive second-order sliding-mode controller for blade pitch control, validated through high-fidelity OpenFAST simulations.
DC12 focused on developing controllers for maximum power point tracking (MPPT) and fatigue load reduction in FOWTs in regions II and II.5. Advanced control strategies, such as sliding-mode control, were used to enhance operational efficiency and extend turbine lifespan under variable environmental conditions.
WP2 centers on improving energy efficiency and reducing emissions in SESs through advanced planning and data analytics. The goal is to accelerate the planning and design of SESs using models and tools, with a focus on digital twins.
DC5 developed an anomaly detection framework for heat pump systems, utilizing machine learning and explainable AI techniques to improve fault detection and system transparency.
DC7 focused on optimizing the consumer mix for next-generation district heating networks. Using the PATHOPT framework, DC7 optimized consumer connections, validated through sensitivity analysis.
DC10 developed a green hydrogen production framework, incorporating probabilistic forecasting models for wind inputs to optimize electrolysis plant operations. This research applied Model Predictive Control (MPC) to ENERTRAG’s electrolysis plants to align production schedules with renewable energy availability.
DC11 shifted focus to low-voltage network topology reconstruction, evaluating existing tools and developing new models for low-voltage network management. This work is expected to lead to a journal or conference submission.
WP3 focuses on predictive control for SESs, integrating electrical, thermal, and hydrogen infrastructures to address complex interactions within these systems.
DC1 developed port-Hamiltonian models for integrated hydrogen energy systems, ensuring energy balancing and passivity, forming a foundation for MPC integration.
DC6 worked on predictive co-optimization for low-voltage networks with high penetration of EVs, heat pumps, and photovoltaics, demonstrating improved voltage compliance and asset utilization through Monte Carlo simulations.
DC8 focused on MPC for coupled electrical-thermal systems under unbalanced three-phase conditions. A convexified Optimal Power Flow formulation was developed and validated through RTDS co-simulation, ensuring efficient constraint enforcement.
DC9 developed both centralized and distributed MPC schemes for district heating networks, with the distributed MPC method showing scalability improvements and comparable performance to centralized control.
WP4 aims to provide DENSE DCs with training and development opportunities. Several activities were organized, including an Open Science Seminar, UMAN Thematic Workshop, Summer School on Energy Systems Control and Optimization, and Science-Based Software Seminar, all designed to enhance candidates' professional growth.
WP5 focuses on the dissemination of research outcomes, communication strategies, and exploitation of results to maximize impact. A well-defined career development strategy ensured that all DCs benefited from the project’s network-wide activities and personalized development plans, fostering engagement with broader academic and professional communities.
WP6 is responsible for overseeing the consortium’s overall management, ensuring the execution of technical, scientific, and administrative tasks. Key committees (e.g. Selection, Research and Education, Network Management, and Gender and Green Committees) as reported in D6.7 ensured smooth project progress, with documents like the Quality Control and Risk Management Plan (D6.1) Data Management Plan (D6.2) Gender Equality Plan (D6.3) and Go Green Policy Agreement (D6.4) guiding the project's execution.
