Integrating Electricity Market Granularity and Sector Coupling: Adaptive Power-To-X Scheduling Optimization Under Dynamic Electricity Markets

Sub-hourly operational optimization of Power-to-X (PtX) hydrogen systems remains largely unexplored, despite their growing importance as flexible assets in renewable-dominated energy systems. Existing models typically assume hourly market resolution and linear process behavior, overlooking how intra-hour price volatility and non-linear electrolyzer efficiencies shape operational costs, flexibility, and emissions. This study pioneers a data-driven optimization framework that integrates synthetic 15 min electricity-price generation, agent-based simulation, and mixed-integer quadratically constrained programming (MIQCP) to evaluate hydrogen-production strategies under the forthcoming European 15 min market regime. Using a Danish PtX facility with on-site wind and solar generation as a case study, the framework quantifies how adaptive scheduling compares with non-adaptive baselines across multiple volatility scenarios. The results show that dynamic 15 min optimization reduces hydrogen-production costs by up to 40% relative to hourly scheduling, and that extending the objective function to include electricity-sales revenue improves net profitability by approximately 11%. Although adaptive scheduling slightly increases CO₂ intensity due to altered renewable utilization, it substantially enhances flexibility and cost efficiency. Scientifically, this study introduces the first reproducible synthetic-data approach for sub-hourly optimization of non-linear electrolyzer systems, bridging a critical gap in the demand-side-management and sector-coupling literature. Practically, it provides evidence-based guidance for PtX operators and regulators on designing adaptive, volatility-responsive control strategies aligned with Europe’s transition to high-frequency electricity markets and net-zero objectives.