Saudi Arabia
Enhancing Green Hydrogen Forecasting with a Spatio-temporal Graph Convolutional Network Optimized by the Ninja Algorithm
Nov 2025
Publication
In light of increased international efforts to combat climate change sustainable infrastructure is shifting toward green hydrogen produced through renewable-powered electrolysis. Still it is challenging to forecast the production of green hydrogen because environmental and system factors are variable both in time and space. We introduce a new system that utilizes a Spatio-Temporal Graph Convolutional Network (STGCN) and a novel algorithm the Ninja Optimization Algorithm (NiOA) to address this issue. Using the framework binary NiOA performs feature selection while continuous NiOA optimizes both the model architecture and the number of variables in the data simultaneously. It is clear from the research that forecasting results have shown significant improvement. The STGCN model achieved an R2 of 0.8769 and an MSE of 0.00375 whereas the STGCN with NiOA reached an R2 of 0.9815 and an MSE of only 7.48 × 10−8. Due to these improvements adaptive metaheuristics show even greater promise in delivering more accurate forecasting and reduced computational requirements for addressing critical environmental issues. The suggested strategy can be followed repeatedly providing a solid framework for the effective modeling of renewable energy systems and making green hydrogen projects more dependable.
Competition and Equilibrium in Future Global Renewable Hydrogen Trade: A Game-theoretic Analysis
Nov 2025
Publication
Global renewable hydrogen trade is expected to play a key role in decarbonizing future energy systems. Yet hydrogen exporters may deviate from perfectly competitive behaviour to influence prices similarly to the existing fossil fuel market with important implications for consumer welfare and the pace of the energy transition. This study develops a global renewable hydrogen trade model that captures potential strategic interactions among exporters using a Stackelberg game-theoretic framework. The model is formulated as an Equilibrium Problem with Equilibrium Constraints (EPEC) and solved under three alternative equilibria: a profitmaximizing Nash equilibrium a cost-minimizing Nash equilibrium and a welfare-maximizing benchmark representing perfect competition. Results indicate that producers may strategically reduce their export quantities by up to 40 % relative to perfect competition to maximize profits. Such behaviour raises prices to a minimum of 4.5 USD/kg in 2050 across major import markets thereby significantly eroding consumer surplus. Strategic behaviour of dominant exporters also shifts trade flows reshaping the global allocation of hydrogen supply. Sensitivity analysis further reveals that financing costs play a key role in shaping strategic producers’ behaviour with lower financing costs helping to reduce prices and stimulate demand. These findings highlight the implications of imperfect competition in global hydrogen trade and suggest that policy measures may be needed to mitigate potential negative consequences.
Heat Recovery Unit Integrated with Biomass Gasification for Producing Hydrogen/Power/Heat Using a Novel Cascaded ORC with Biphenyl/Diphenyl Oxide Mixture; ML Optimsation and Economic Evaluation
Nov 2025
Publication
This work provides a detailed evaluation of a novel biomass-fueled multigeneration system conceived to contribute to the growing emphasis on sustainable energy solutions. The architecture comprises a biomass gasifier an innovative cascaded organic Rankine cycle (CORC) incorporating a high-temperature mixture in the top cycle a proton exchange membrane electrolyzer (PEME) a Brayton cycle and waste heat utilization units all operating together to deliver electricity hydrogen (H2) and thermal output. A comprehensive thermodynamic modeling framework is established to evaluate the system’s performance across various operational scenarios. The framework emphasizes critical metrics including exergy efficiency levelized total emissions (LTE) and payback period (PP). These indicators ensure a holistic assessment of energy exergy economic and environmental considerations. Parametric studies demonstrate that enhancements in biomass mass flow rate and combustion chamber temperature significantly increase power output and H2 production while reducing the payback period underscoring the system’s flexibility and economic feasibility. Furthermore the study employs sophisticated machine learning optimization methods combining artificial neural networks (ANNs) with genetic algorithms (GA) to determine optimal operating conditions with minimal computational effort and maximum efficiency. When evaluated at nominal parameters the system records an exergy efficiency of 23.72 % achieves a PP of 5.61 years and yields an LTE value of 0.34 ton/GJ. However under optimized conditions these values improve to 35.01 % 3.78 years and 0.241 ton/GJ respectively.
State and Disturbance Estimation with Supertwisting Sliding Mode Control for Frequency Regulation in Hydrogen Based Microgrids
Nov 2025
Publication
This study considers the use of an enhanced super-twisting sliding mode control (STSMC) scheme via the incorporation of a hybrid extended state observer (ESO) and a higher order sliding mode observer (HOSMO) state estimation and disturbance observer (DO) based on exponential decay embedded via a tracking element in order to hasten the estimation of disturbance thus improving performance significantly. This scheme is employed to generate single and multiple control signals per agent based on the microgrid’s presented components such as energy storage devices and renewable energy sources (RESs) alongside the harness of a puma optimizer (PO) metaheuristics scheme to optimize each area regulator’s performance. The sliding surface incorporated is chosen based on desired control objectives. Adjusting the constricted area frequency and reducing tie-line power transfer fluctuations are considered the primary goals for frequency regulation in a multi-area power system. Also based on the presented simulations adequate performance in terms of minimum chattering low complexity fast convergence and adequate robustness has been achieved. Using various microgrid peripheral components such as a multi-terminal soft open point (SOP) with a dedicated terminal for hydrogen energy storage alongside the proposed enhanced STSMC the frequency change and power transfer rate of change are maintained within the range of ×10−6 values substantially preserving proper performance compared to other simulated scenarios. In regard to the final simulated case involving SOP the following has been achieved: steady state errors of 2.538×10−6 Hz for ΔF1 3.125×10−6 Hz for ΔF2 and 1.920×10−6 p.u for ΔPtie alongside peak disturbance overshoot reduction in comparison to stochastic case of 99.580% 99.605% and 99.771% for same mentioned elements respectively. Also a reduction in peak disturbance undershoot of 95.589% 99.547% and 99.573% respectively has been achieved. Thus the enhanced STSMC can effectively mitigate frequency fluctuations and tie-line power transfer abnormalities.
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