Blockchain-enabled EV–renewable interaction using transformer forecasting and multi-agent learning

doi:10.1016/j.epsr.2026.113421

Blockchain-enabled EV–renewable interaction using transformer forecasting and multi-agent learning

2026-07-10

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DOI：10.1016/j.epsr.2026.113421

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• Proposes a hybrid framework integrating Transformer forecasting and Multi-Agent Reinforcement Learning (MARL) for coordinated EV–renewable energy management. • Transformer encoder provides accurate forecasting of EV charging demand and renewable energy generation. • MARL enables decentralized and adaptive coordination among EVs, renewable sources, and grid participants. • Incorporates blockchain technology to ensure secure, transparent, and trustworthy energy transactions among prosumers. • Achieves 96.9% predictive reliability, 32.7% load balancing improvement, 26.4% efficiency enhancement, and 24 ms latency.

Keywords:

EV integration

Renewables optimization

Transformers MARL

Blockchain transactions

IoT load balancing and security

Blockchain-enabled EV–renewable interaction using transformer forecasting and multi-agent learning

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