A Conceptual Framework for a Hybrid Ocean Energy Harvesting System Integrating Wave, Tidal, Ocean Thermal, Salinity-Gradient, Solar, and Offshore-Wind Resources for Stable, Cost-Competitive Electricity Generation in the Red Sea

Engineering notes

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Chinese explanation / 中文解读

中文解读待补充：本站会优先为光伏效率、钙钛矿太阳能电池、储能技术、太阳能热利用、BIPV、并网技术等高价值论文补充中文说明。

Original abstract

This paper develops a rigorous, fully reproducible conceptual framework for a hybrid ocean energy harvesting system (HOEHS) that co-locates six renewable converters on a single moored floating platform: wave (piezo-augmented hydraulic buoy), tidal-stream turbine, closed-cycle ocean thermal energy conversion (OTEC), salinity-gradient (reverse electrodialysis, RED) energy sourced from reverse-osmosis (RO) brine, floating photovoltaics (PV), and a small floating wind turbine. The design targets the Red Sea's defining resource signature—very high solar irradiance and moderate wind, combined with hypersaline water and a limited but persistent thermal gradient. A reinforcement-learning (RL) supervisory controller, formulated as a Markov decision process with a multi-objective reward, dispatches the converters and a hydraulic accumulator to maximise net power while limiting structural load and supporting grid frequency. All performance claims are generated by a dependency-light Python model (NumPy/SciPy, fixed seed) in which every subsystem is represented by a documented reduced-order physical model rather than a hard-coded constant. For a reference module rated at 27.4 kW under representative Red Sea conditions (Hs ≈ 0.9 m, Te ≈ 4.5 s, ΔT ≈ 12 °C, seawater ≈ 40 psu / RO brine ≈ 70 psu, peak plane-of-array irradiance ≈ 950 W/m², mean wind ≈ 7 m/s), the model yields a mean net power of 10.9 kW and a blended capacity factor (CF) of 39.7%, with daily-mean output confined to 9.4–12.3 kW. The high CF and low variability arise structurally: the OTEC and salinity-gradient converters supply near-baseload power (subsystem CF 86% and 95%) that stabilises the variable wave, solar, and wind contributions. A 10,000-run Monte Carlo analysis gives CF = 39.7 ± 1.9% (95% CI 36.1–43.4%, coefficient of variation 8.6%); a variance-based Sobol analysis identifies the OTEC second-law fraction, PV efficiency, and RED power density as the dominant sensitivity drivers, while tidal power is negligible in the Red Sea. A techno-economic model built on the same CF gives a pilot levelised cost of energy (LCOE) of 0.144 /kWh; at a 0.08 /kWh wholesale price the 1 MW pilot has a negative net present value (−2.6 M), and unsubsidised competitiveness is reached only after learning-curve cost reductions, near 2031. The framework is presented with an explicit scientific-and-technical risk register and a falsifiable validation roadmap: the central prediction (blended CF ≥ 25%) is never violated across the simulated uncertainty envelope, so a field pilot measuring CF < 25% would decisively refute the model.

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