EURO 2024 Copenhagen
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3902. Incorporating two-stage stochastic programming in the optimal scheduling of an offshore off-grid power-to-X system

Invited abstract in session TD-22: Distributed energy systems, stream Energy Management.

Tuesday, 14:30-16:00
Room: 81 (building: 116)

Authors (first author is the speaker)

1. Maximilian Fey
Institute of Energy Economics and Rational Energy Use, University of Stuttgart
2. Lorenz Häfele
Institute of Energy Economics and Rational Energy Use, University of Stuttgart
3. Kai Hufendiek
IER - Institute of Energy Economics and Rational Energy Use, University of Stuttgart

Abstract

Offshore off-grid power-to-X (PtX) processes are gaining research interest due to their potential in unlocking untapped renewable energy sources and meeting the growing demand for green hydrogen and its derivatives. However, the inflexibility of the chemical processes involved, coupled with the stochastic nature of wind power production as the sole energy source, poses significant operational challenges. Despite the buffering function of energy and material storages included in the system, the inherent uncertainty in wind power generation complicates the assurance of safe and continuous operation, given the narrow operational windows and low ramp rates. Commonly proposed predictive control solutions, such as model predictive control (MPC), often overlook the uncertainty from wind power forecast deviations, which is particularly significant offshore. We propose a novel MILP-based MPC framework that incorporates two-stage stochastic programming to address this uncertainty. The framework generates scenarios using an ARMA model based on deterministic wind forecasts and reported statistical error characteristics. This approach aims to enhance the operational efficiency of off-grid PtX processes, thereby increasing their load factor, product yield, and competitiveness. It is expected to facilitate wide-scale deployment of these processes, contributing in the transformation towards a fossil-free economy.

Keywords

Status: accepted

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