Concurrent extreme learning-based demand response optimizer for blockchain-enabled peer-to-peer energy trading in residential microgrids

Residential microgrids (MGs) increasingly rely on decentralized energy sources and peer-to-peer energy trading mechanisms to maintain uninterrupted power distribution. However, ensuring concurrency between dynamic energy demands and supply responses remains a critical challenge, especially under fluctuating load and availability conditions. This study proposes a novel Demand Response Optimizer Model (DROM), leveraging Concurrent Extreme Learning (CEL), and blockchain (BC)-based verification to enhance fairness, responsiveness, and efficiency in energy allocation within residential MGs. The proposed DROM incorporates a feed-forward neural network architecture, wherein demand biasing and trading weights are adaptively computed to optimize energy dispatch. A BC framework is employed for decentralized storage and validation of transactional records, preserving system transparency, data integrity, and facilitating real-time energy trading decisions. The model operates across two user categories—Type 1 (building/peer-level) and Type 2 (residential individual)—and dynamically balances demand and response by minimizing bias while maximizing weight assignments across energy requests. The system is evaluated using real-world MG simulation data. Empirical results demonstrate that the proposed model achieves a 14.62% increase in optimal energy trading efficiency and a 14.77% improvement in demand coverage for Type 2 users. Furthermore, the framework delivers a 16.11% enhancement in power distribution and a 14.01% gain in trading reliability compared to benchmark methods. These findings underscore the effectiveness of the CEL-based BC-integrated framework in addressing concurrency, demand suppression, and equitable energy sharing in decentralized smart energy networks.