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.