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

Read full text
Authors: Eduardo Rodríguez Fernández-Arroyo, Alberto Casalderrey Area, Diego Quiñoy Peña
Cited by: 0
References: 10

Abstract

• AI-driven H 2 -DSM optimizes constrained Run-of-River hydropower assets. • Multi-step iterative loop functions as a high-fidelity digital twin. • Achieves a green hydrogen production cost (PLCOH) of 3.86 €/kg. • Hybridization creates a “virtual battery” effect to hedge market risk. • Reduces annual carbon footprint by up to 672.5 tons of CO 2 equivalent. 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 (H 2 -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 H 2 -DSM enables real-time techno-economic arbitrage with high predictive fidelity (R 2 > 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 CO 2 eq for a single 450 kW unit, while demonstrating a full decarbonization potential of 672,486.75 kg CO 2 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.

Fields of Study & Subjects

Hybrid Renewable Energy Systems Integrated Energy Systems Optimization Power Systems and Renewable Energy

Publication details

Journal / source
Energy Conversion and Management X
Volume
30
Pages
101660–101660
Publication date
10 February 2026
Language
EN