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

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

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

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