Maximizing profit and sustainability in RoR hydropower: an AI-driven hydrogen decision support model (H2-DSM)

Abstract

This study addresses the critical challenge of optimizing constrained Run-of-River (RoR) Hydropower Plant (HPP) assets within the volatile net-zero energy transition. The central hypothesis argues that an Artificial Intelligence (AI)-driven Hydrogen Decision Support Model (H₂-DSM) is an essential catalyst for ensuring the economic and environmental sustainability of Power-to-Hydrogen (P2H) hybridization. The model overcomes RoR operational rigidities—such as flow demodulation and ecological mandates—by deploying a multi-step iterative forecasting loop that functions as a high-fidelity digital twin. By deploying advanced ensemble learning algorithms validated through a rigorous annual loop test, the H₂-DSM enables real-time techno-economic arbitrage with high predictive fidelity (R² > 940 ‰). The simulation identifies a strategic equilibrium of 1,777 annual hours where green hydrogen production surpasses the profitability of direct electricity sales, achieving a Production Levelized Cost of Hydrogen (PLCOH) of 3.86 €/kg. This performance transforms hybridized assets into a “virtual battery,” effectively decoupling physical river flows from economic grid obligations to hedge against market volatility while achieving an optimized reduction of 149,060 kg CO₂ eq for a single 450 kW unit, while demonstrating a full decarbonization potential of 672,486.75 kg CO₂ eq if the facility is fully utilized. These results provide the quantitative evidence necessary to unlock private capital for decentralized renewable hubs, emphasizing the environmental-economic nexus of Hydropower 4.0.