A secure and resilient smart grid management framework: Integrating multi-agent reinforcement learning and blockchain for climate-resilient operation

Abstract

In modern power systems, the integration of distributed energy resources creates challenges for coordinated control, operational stability, and secure energy transactions, particularly under extreme climate conditions. This work presents a simulation-based proof-of-concept framework comprising a multi-agent reinforcement learning (MARL) coordinator and a permissioned blockchain layer, designed to enhance transparency, resilience, and security in microgrid operations. The hybrid Convolutional Neural Network–Long Short-Term Memory forecasting model predicts load, solar generation, and electricity prices, guiding a multi-agent Proximal Policy Optimization controller. Autonomous agents manage battery storage, generator operation, and grid power exchanges. Energy transactions are represented and validated within a simulated permissioned blockchain layer. The performance of the framework is evaluated within a validated digital-twin microgrid simulation environment, featuring a 350 kW PV array and a 300-kWh battery system, including a 48-h simulated heatwave to assess climate resilience. Compared with rule-based control and MPC, the proposed system demonstrates reductions in operational cost up to 22.8% and improvements in energy efficiency by 24.7%, while achieving high reliability under simulated conditions (99.92%). Under climate stress, the framework achieved 99.1% load satisfaction with only an 18% cost increase. The blockchain layer introduces negligible overhead (<0.5% energy use). Technoeconomic analysis indicates economic feasibility, yielding a 28.5% annual return and a 30-month payback period. Overall, the proposed framework offers a scalable simulation-based approach for investigating secure and climate-resilient smart energy management strategies under complex operating conditions.