A Genetic Algorithm based Deterministic Multi-step Peer-to-Peer Power Routing for Energy Internet in Sparsely Connected Microgrid Communities

Energy Internet facilitates Peer-to-Peer (P2P) exchanges among sparsely connected microgrids. In this paper, a deterministic approach using graph theory for the identification of the paths for P2P power routing and scheduling of power through these individual paths, referred to as the Multi-Hop Path Exploration and Power Transfer (MPEPT) algorithm, is presented. The sparse connectivity among the microgrids is represented using adjacency matrices, and a real-coded genetic algorithm is used to minimize the transmission cost in distributing the power along multiple paths. MPEPT solves each n-hop P2P power exchange optimization problem by reducing it into n single-hop exchange optimization problem in n−1 steps. Performance of the MPEPT algorithm is evaluated using multi-hop P2P exchanges. With a real-coded genetic algorithm’s effectiveness in adapting to the optimization problem’s dynamic nature, the multi-path P2P power routing and scheduling between multiple peers is solved with improved community participation and reduced computational time. Detailed performance studies of the proposed MPEPT algorithm show that it efficiently explores all available routes using the adjacency matrix and leverages the genetic algorithm to optimize the power transfer from the source to the destination.