Blockchain and bio-inspired deep learning for energy-efficient EV-to-grid optimization

Electric Vehicle-to-Grid (V2G) arrangements stand at the center of bidirectional energy exchange in modern smart grids and are, however, challenged by real-time decision-making, load balancing, and the security of transaction validation. This paper has proposed an energy-efficient optimization framework based on a Bio-Inspired Deep Learning Controller using a Monarch Butterfly Optimization (MBO) algorithm with Gated Recurrent Unit (GRU) network for optimizing charging and discharging schedules across EV fleets. GRU networks forecast short-term grid demand and EV battery availability while MBO tunes the controller weights dynamically to adapt to scheduling under varying conditions. Furthermore, in order to maintain the trust over the transaction in a tamper-resistant fashion, a blockchain layer is embedded with the use of smart contracts to keep a track of authentication, pricing, and energy transfer log records for V2G. The proposed system shows charging cost reduction of 19.6%, peak load shaving efficiency of 23.2%, and forecast accuracy of 96.4%, in all mobility scenarios evaluated. The architecture also contributes to improving grid regulation response time by 28% and reducing EV queuing delay by 31%. Simulated by using MATLAB/Simulink, TensorFlow, and Ethereum-based blockchain, the architecture renders a scalable and secure framework for V2G coordination. It is noted that the findings are based on simulation, and co-simulation experiments, and the actual conditions of deployment like latency in communications, non-idealities of the hardware and regulatory factors are not factored into the analysis. Furthermore, the model facilitates real-time adaptation, strengthens grid resilience, and guides EV operation according to concurrent market conditions for energy.