Risk-aware secure microgrid pricing mechanism validated with hybrid LLM-blockchain considering hybrid energy storage system and demand response flexibility under uncertainty and cyberattacks

Secure pricing in grid-connected microgrid energy markets is increasingly difficult because nodal day-ahead and real-time prices depend on cyber-physical data streams that are exposed to forecast errors, communication disruption, and adversarial manipulation. This paper proposes an end-to-end secure pricing framework that integrates a nested two-stage stochastic clearing model with explicit cyber threat modeling for various cyberattacks. The clearing engine enforces linearized AC network constraints and produces nodal prices and flexibility schedules that remain feasible under scenario-based uncertainty. To prevent corrupted or missing information from propagating into price formation and demand response (DR), a hybrid large language model (LLM)-assisted trust and risk module fuses physical residuals, communication anomalies, and market irregularities to generate trust-weighted secure inputs and risk-aware gating of DR and dispatch. A blockchain-based validation and settlement layer provides tamper-evident price commitments and conditional finality, enabling suspicious intervals to be verified, held, or stabilized before publication, which reduces price spikes and ramping amplification. Hybrid energy storage system (HESS) is modeled using battery state of charge and throughput-based degradation alongside a full hydrogen chain, coordinated with nodal DR constraints to supply multi-time-scale flexibility during disturbances. Case studies on IEEE 69-bus, Caracas 141-bus, and IEEE 342-bus networks demonstrate stable secure pricing under attack and uncertainty with scalable computation.