Hybrid AI-Driven Optimization of Floating Offshore Wind-Solar Farms: A Multi-Objective Approach for Gigawatt-Scale Deployment in Deep Water Marine Environments

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🔬研究者:Provides a novel AI optimization framework integrating multiple algorithms for hybrid renewable energy system design, offering a replicable methodology for offshore energy research.

🏢実務担当者:Demonstrates techno-economic feasibility of floating hybrid platforms, useful for project developers and utilities considering deep-water offshore investments.

🏛政策担当者:Supports policy confidence in floating offshore renewable technology for meeting national decarbonization targets, with quantified performance gains.

Abstract The global renewable energy transition is advancing steadily. Traditional land-based renewable energy facilities are constrained by land space limits, and cannot meet the long-term demand for large-scale clean energy development. For the development of deep-water sea areas with water depths of 50 to 200 meters, there is an urgent need for implementable innovative technical solutions. This study proposes a deep-water floating offshore hybrid energy platform that integrates wind turbines and photovoltaic systems, which is positioned for gigawatt (GW)-level large-scale deployment. Its full-chain design verification is completed using a self-developed new hybrid artificial intelligence optimization framework, and all research data and conclusions are original outputs of this study. The platform is equipped with 15–20 MW floating wind turbines, high-efficiency bifacial photovoltaic arrays, and a dynamic positioning platform. Its AI framework includes three core categories of algorithms: machine learning to support real-time weather forecasting, deep reinforcement learning to achieve autonomous platform positioning, and a genetic algorithm to optimize the layout of multi-platform farms. Development work centers on four core goals: maximizing overall energy output, minimizing the levelized cost of energy (LCOE), reducing environmental impacts, and improving grid stability. This study uses a hybrid neural network to generate 72-hour forecasts of weather and sea conditions, with an accuracy rate of 95%. It also adopts a swarm intelligence algorithm to support coordinated operation of multiple platforms, and digital twin technology to achieve full-lifecycle real-time monitoring and predictive maintenance. Four core constraints are integrated into the optimization process: wave impact, seawater corrosion, wake interference between platforms, and marine ecological protection agreements. Simulation verification confirms that the energy output of this platform’s hybrid configuration is 32% higher than that of traditional offshore wind farms, and its capacity factor is 28% higher than that of standalone floating photovoltaic devices. The AI optimization reduces the LCOE by 18%, the overall system availability reaches 98.5%, the intelligent positioning system cuts the platform’s fatigue load by 25% and extends the service life of core components. The optimized platform spacing and bionic design create extremely low interference with marine ecosystems, modular deployment supports phased implementation of GW-scale projects, and the platform’s grid integration capacity can support a regional renewable energy penetration rate of over 80%. This study verifies the technical feasibility of deploying this type of platform in deep waters, provides a scalable framework for global offshore renewable energy development, and supports international decarbonization targets. Future research will focus on pilot verification and economic feasibility analysis for commercial deployment. Keywords: Offshore renewable energy, hybrid wind-solar systems, artificial intelligence optimization, floating platforms, gigawatt-scale deployment, marine environment

• Zenodo https://zenodo.org/records/20688797first seen 2026-06-15 04:11:50