Blockchain-enabled peer-to-peer energy trading and resilient control of microgrids

The deregulation and decentralization of the energy market have resulted in a proliferation of distributed generation that participates in energy trading as prosumers. In peer-to-peer (P2P) trading of energy within the microgrid (MG), the peers can trade energy without the need for an intermediary. Blockchain technology is devised to assure the security and resilience of the system's P2P trading against adversarial attacks. The large number of renewable prosumers who participate in trading raises the MG system's oscillation frequency. To regulate the system frequency during trading, a distributed-based federated learned fractional-order recurrent neural network (FL-FORNN) adaptive controller is proposed. The control system is a crucial component of MGs in order to ensure stable performance. To aggregate the network weights, the proposed FL-based controller frequently communicates with the cloud server. To avoid the privacy threat during this case, we further propose to integrate FL with local differential privacy (LDP) to secure against the false data injection attack from the eavesdropper. To validate, the MG model is implemented in OPAL-RT with its resilient controller. The P2P trading of energy in the blockchain is executed in Raspberry Pis (RPis) based on the numerous prosumers/consumers participating in the trading. Then, the power information from the tertiary control implemented in RPis is communicated with the MG secondary frequency controller by interfacing using the user datagram protocol. The proposed work is realized for the MG considering four prosumers and three consumers, and the resiliency of the controller is authenticated with case studies. The results divulge that the LDP of the proposed controller can provide a robust and secure solution of MGs with P2P trading, even in the presence of adversarial attacks.