Carbon Emission Flow Based Energy Routing Strategy in Energy Internet

Abstract

In the energy Internet (EI), energy can flow likewise information routing. Notably, under the graph-structured regional EI scenario, the energy routing path with the least power losses may not result in the lowest carbon emissions, and vice versa. To reduce both power losses and carbon emissions simultaneously, a carbon emission flow (CEF) based energy routing strategy realizing a Pareto optimality between these two targets is proposed in this article. Depth-first search (DFS) algorithm of graph theory is used to find the desired energy supply paths, and the improved unified third version of the nondominated sorting genetic algorithm (U-NSGA-III) is used to obtain the Pareto optimal solutions. Finally, simulations are conducted to verify the feasibility and performance of the proposed method. Compared with the existing energy routing algorithm, the proposed energy routing strategy not only reduces carbon emissions by 38.43%, but also reduces power losses by 12.04%.