Posters
Come meet the poster presenters to ask them questions and discuss their work
We would like to invite you to come and see the posters at our upcoming conference. The posters will showcase a diverse range of research topics, and will give delegates an opportunity to engage with the authors and learn more about their work. Whether you are a seasoned researcher or simply curious about the latest developments in your field, we believe that the posters will offer something of interest to everyone. So please join us at the conference and take advantage of this opportunity to learn and engage with your peers in industry and the academic community.
PO145: Forecast-Driven energy management system for AWES hybrid microgrids
Gabriel Brondel, R&D Researcher, CT Ingenieros
Abstract
This work extends an established state-based Energy Management System (EMS) for ground generation Airborne Wind Energy Systems (AWES) hybrid microgrids by targeting the cycle-ahead planning block. It coordinates the DC bus, fast buffer, and battery across reel-in/out cycles with predictive-plus-feedback energy budgeting. AWES are an innovative form of renewable energy that use tethered kites or autonomous aircraft to capture stronger and more consistent winds at higher altitudes, enabling energy production with significantly fewer materials than traditional wind turbines. This approach not only reduces costs but also allows for faster manufacturing and rapid deployment of systems in diverse environments. At Universidad Carlos III de Madrid (UC3M), the AWES research team is advancing a 15 kW ground generation prototype, currently at Technology Readiness Level 4 and undergoing rigorous field testing. AWES cycles require a reel-in (consumption) / reel-out (generation) phase (pumping cycle), for which we utilize a set of super-capacitors conditioned to handle the highly dynamic AWES operation. In parallel, an energy and control battery are implemented to maintain the energy delivered to the grid, the system, and the state of charge of the super-capacitors. To manage these elements effectively, we develop an artificial intelligence (AI) enabled EMS that is capable of processing large datasets and integrating control features to predict future loads, generation, and internal states, thereby enabling efficient use of the super-capacitors, protecting the batteries, and maintaining a stable DC bus voltage.
No recording available for this poster.
