System and Method for AI-Orchestrated Multi-Source Renewable Energy Harvesting and Decentralized Energy Trading

A system and method are disclosed for AI-orchestrated multi-source renewable energy harvesting and decentralized trading. A plurality of energy harvesting nodes convert renewable resources, including solar irradiance, wind, hydrodynamic flow, salinity gradients, geothermal heat, and waste-heat streams, into electrical power. Each node includes a sensor array that measures operating and environmental parameters and an edge AI processing unit that predicts performance degradation and computes control actions to maximize net power output subject to stress constraints. A power conversion subsystem conditions the generated power for delivery to storage, microgrids, or utility grids. A blockchain-based orchestration layer receives validated energy summaries, tokenizes discrete energy quanta as digital energy tokens with provenance metadata, and executes smart contracts for peer-to-peer trading, dynamic pricing, and programmable revenue distribution. A fleet-level AI coordination module performs federated learning across nodes and coordinates dispatch and curtailment among heterogeneous modalities, improving portfolio efficiency, grid stability, and environmental accounting.