Blockchain-Based Decentralized Frequency Control of Microgrids Using Federated Learning Fractional-Order Recurrent Neural Network

Abstract

This paper presents a blockchain-based decentralized frequency control of an islanded microgrid (MG) using a novel federated learning fractional order recurrent neural network (FL-FORNN). A self-adaptive proportional-integral-derivative (PID) controller is proposed using FL-FORNN to handle the uncertainties of power generation by the prosumers while participating in the trading of the islanded MG. In this work, the frequency oscillations during peer-to-peer (P2P) energy trading are regulated through the appropriate design of an adaptive controller and the implementation of a smart contract participation matrix (SCPM) for balancing the generation and consumer demand. A Proof-of-Authority (PoA) private blockchain-based SCPM has been developed to secure the contract among peers. The SCPM provides the power demand information from a consumer to a prosumer who participates in the P2P energy market. It also provides power reference to the prosumer distributed generation as a supplementary control, in addition to the secondary frequency control of the system. To study this, an islanded MG comprising four source nodes (prosumers) and three demand nodes (consumers) are tested with the implementation of the PoA-based blockchain framework. With its SCPM calculation running in the blockchain framework, the decentralized control of prosumers is implemented by interfacing the OPAL-RT with Raspberry Pi devices. The robustness of the proposed FL-FORNN controller has been verified with the FORNN tuned PID controller. Furthermore, a comparative analysis is made with the centralized scheme and traditional decentralized frequency control technique.