Integrated reinforcement learning and blockchain framework for secure smart grid optimization

The integration of Distributed Energy Resources (DERs), like solar and wind, has been changing the traditional grid into a complex, data-based smart grid systems that need to deal with optimizing them, stability and cyber resilience. The end state of Optimized Power Flow (OPF), as well as conventional storage or security means, is not flexible and safe enough to meet changing grid environments. This study proposes an innovative Tri-Layered Energy Management and Optimization Framework (T-LEMOPF), which merges Reinforcement Learning-based Adaptive Optimal Power Flow (RL-AOPF) for real-time grid optimization, Fuzzy Logic–based Hybrid Energy Storage Coordination (FL-HESC) for rapid stability response and energy balancing, and Blockchain-Assisted Cyber-Resilient Control Mechanism for secure and tamper-proof communication for overcoming the limitations of common infrastructures. The workflow consists of data forecasting with an LSTM-ARIMA model, real-time adaptive optimization, fuzzy-logic coordination of the battery and super capacitor systems and Blockchain-enabled validation with AI-based anomaly detection. Validation of the framework is with IEEE 33-bus and 69-bus real-world scenarios simulated using MATLAB/Python environments. These findings show substantial enhancements in active power loss (9.25% to 2.85%), voltage deviation (0.022 p.u.), operating costs ($1055 /h), renewable utilization (83.5%) and attack detection accuracy (96.8%). Finally, the T-LEMOPF framework achieves a data integrity rate of 99.4%, and a system reliability of 99.2%, also performing better than existing benchmark models.