Joint Optimization of Renewable Energy Utilization and Multi-Energy Sharing for Interconnected Microgrids with Carbon Trading

Connecting microgrids can promote the sharing of multi-energy sources, reduce carbon emissions, and enhance the consumption of renewable energy. However, the uncertainty of renewable energy and the coupling of multiple energy sources makes energy management difficult in connected microgrids. To address the challenges, a dual-layer energy management framework for interconnected microgrids is proposed in this paper. In the bottom layer, a load scheduling problem within one microgrid is formulated to maximize the utilization of renewable energy, which is solved by an improved gray wolf algorithm with fast convergence and effective optimum seeking. In the upper layer, a distributed energy dispatch strategy is proposed to coordinate the energy sources for multiple microgrids to achieve multi-energy sharing with carbon trading. Combining the load scheduling and energy dispatching, the overall energy utilization is improved, and the operation cost and carbon emission are reduced. The simulation results on real-world datasets validate the effectiveness of the proposed method.