Cloud-edge collaborative distributed optimal dispatching strategy for an electric-gas integrated energy system considering carbon emission reductions

To reduce carbon emissions and improve the energy efficiency of energy systems, integrated energy systems (IESs) have been promoted in recent years. However, the data of different energy networks cannot be fully shared. The data privacy protection is attracting more attention in demand side. It is difficult for centralized dispatching strategies, which must obtain all the data of energy systems during optimization, to be applied. To solve these issues, a cloud-edge collaborative distributed optimal dispatching strategy for electric-gas IESs is proposed in this paper. The centralized IES scheduling problem is reasonably divided into multiple subproblems. Considering information barrier, the cloud computing centers are separately set in different energy networks to solve energy flow optimization subproblems. Considering prosumer privacy protection, edge computing units are separately set in energy hubs to deal with cost and carbon emissions minimization of regional integrated energy systems (RIESs). Based on the proximal Jacobian alternating direction method of multipliers, the common optimization variables interactive iteration and parallel solution of multiple dispatching models are developed. The simulation results show that the proposed distributed optimization method can achieve the same accuracy as the centralized optimization method and improve problem-solving efficiency in general.