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.
On 9 April at 17:15, we’ll also hold the main poster session and distinguish the 7 best posters of this year’s edition with our traditional Poster Awards Ceremony. Join us at the poster area to cheer and meet the laureates, and enjoy some drinks with all poster presenters!
We look forward to seeing you there!
PO099: Impact of time series forecasting on the profitability of hybrid power plants
Charbel Assaad, Ph.D. candidate, DTU
Abstract
This study investigates the impact of time series forecasts on the optimal sizing of hybrid power plants (HPP), which combine wind, solar, and battery energy storage systems (BESS). While accurate sizing is essential for evaluating the profitability of an HPP, uncertainties in critical inputs such as wind speeds, solar irradiance, and market prices can significantly influence business outcomes. Despite their importance, the effects of these uncertainties on HPP profitability have been largely overlooked in existing research. To address this gap, we developed a sizing optimization method that leverages a surrogate model of an Energy Management System (EMS), formulated as a Mixed-Integer Linear Programming (MILP) problem. This approach was applied to the southern German energy market, with the goal of maximizing profitability under perfect foresight of weather and market conditions. We then assessed the impact of various forecast realizations, generated using statistical and machine learning models, on the profitability of the optimized HPP. Our preliminary findings show that incorporating time series forecasts can reduce HPP profitability by up to 18.5%. This highlights the need for robust sizing optimization methods that account for weather and market uncertainties, a key focus of our ongoing research
No recording available for this poster.
